",isbn:"978-1-80355-367-2",printIsbn:"978-1-80355-366-5",pdfIsbn:"978-1-80355-368-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"d3a491e5194cad4c59b900dd57a11842",bookSignature:" Vladimir V. Kalinin",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11782.jpg",keywords:"Variety of Traits, Historical Remarks, Modern Definitions and Descriptions, Personality Disorders, Comorbid Psychopathology, Depression, Anxiety, Obsessions, Delusion, Treatment of Personality Disorders, Phenomenology of Personality Traits, Delusional Symptoms",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 9th 2022",dateEndSecondStepPublish:"May 12th 2022",dateEndThirdStepPublish:"July 11th 2022",dateEndFourthStepPublish:"September 29th 2022",dateEndFifthStepPublish:"November 28th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:'A researcher with over 300 publications in psychopathology, psychopharmacology, neuropsychiatry, and epileptology, a member of the Russian Society of Psychiatry, and the Russian Society of Epileptology. Dr. Kalinin\'s biography is included in Marquis "Who’s Who in Medicine and Healthcare" (2006-2007); Who’s Who in Science and Engineering 2008-2009"; "Who’s Who in the World" (2010, 2011), and in the Cambridge International Biographical Centre.',coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"31572",title:null,name:"Vladimir V.",middleName:null,surname:"Kalinin",slug:"vladimir-v.-kalinin",fullName:"Vladimir V. Kalinin",profilePictureURL:"https://mts.intechopen.com/storage/users/31572/images/system/31572.png",biography:"Vladimir V. Kalinin was born in1952 into a family of physicians in Orenburg (Russian Federation). He obtained an MD from Moscow State Medical Stomatological University in 1976. In 1976-1977 he completed an internship in Psychiatry. 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\n\t\t\t
1. Introduction
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Model based predictive controller is a family of control algorithms suitable for some applications. They are useful for most of the real applications. But they are not used very often due to the complexity of them. So this kind of books should be closer to the real application in order to be more used. One of the main objectives, in this chapter, is to clarify the use of the MBPC for real applications.
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My experience is based on real application of MBPC. Additionally they are also suitable for small modification. One of the adaptations explained in this chapter is the one related with disturbance measurement. All real systems have disturbance. Many times the disturbance can be measured and identify. The disturbance measurable can be identified but you can not use this variable as a control variable. You can include this information in the optimization process. One the examples explained in this chapter is the management of this information. In the air management loop in a internal combustion engine use to manage VNT (Variable Nozzle turbine), EGR (Exhaust gas recirculation), SWIRL valve and others, but mass fuel injected is managed by air management loop. But this mass fuel has important influences in the air management process, so it should be considered.
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The philosophy considering disturbance measurable has influences on different parts of the control algorithm development. MBPC has 4 main topics: model, control cost, optimization process and receding horizon. In this chapter the development will be explained.
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Due to the high quantity of control parameters considered in the MBPC, it use to be rejected for using. In this chapter we will try to explain the influence of the most important parameters in order to show the way for tuning.
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2. State of art
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Control Engineering Science is a very brand new Science branch compared with other sciences. In fact the first servo system of the history was developed in 1788 by James Watt. But the strong theoretical development was done during 20th century. Additionally the control engineering science has been developed as a auxiliary tool for other real applications. One example is the development was SMC (Sliding Mode Control), which was developed to control artificial satellites.
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Probably this is the main reason because, control engineering, is mainly considered as a secondary science. In this chapter MBPC will be explained, mainly the GPC (Generalized Predictive Control).
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2.1. Control engineering
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As explained in 1788, James Watt developed the first servo system of the history but the real development of this science was during 20th century. The first important mathematical development were done by Nyquist[27], this work studied stability of the system by means of closing the loop. One of the problems when closing the loop is the possibility of instability, that is why Nyquist developed the theory in 1932. During forties some techniques of frequency design were developed, thus control engineering were widely implemented in the Industry. During fifties Evans[14] proposed the root locus for designing algorithms with stability. From sixties the digital hardware were getting importance so digital or disserted algorithms were implemented. With discrete systems more flexible algorithms and structure were used, even thus the first approached were done by discrimination of classical controllers.
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2.2. Control algorithms MBPC history
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MBPC has its origin in Kalman’s work. He developed an observer in which design an optimization cost is used. Additionally the optimal control based on the optimization of a quadratic cost. This control was the LQG. In comparison with Evans[14] theory, the behaviour of the controlled system is not base on system response, but it is based on the minimum cost of a quadratic cost function.
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The first MBPC published was in 1976 by Richalet[33]. He formalized the development two years later in the Paper. This algorithms is known as MPHC (Model Predictive Heuristic Control), which was commercially developed as IDCOM, this commercial software included identification, predictive controller and impulsion model. One year later Cutler and Ramaker[12] developed de DMC (Dynamic Matrix Controller) based also in the impulsion system response. Both algrothm were known as first generation MPC (Model Predictive Controllers), but neither IDCOM nor DMC were based in LQG models or state space models.
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During first half of eighties the second generation of algorithms was developed. DMC with constraints were presented by García[16] by using Quadratic Programming QP in the optimization process. Connoisseur from Invensys Corporation is also an algorithm of this family.
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The most important advance in MBPC were developed during second half of eighties and nineties were third generation appeared. In 1988 Keyser[20] compared some MBPC adaptative. Shell company worked in the first MBPC based in state space, its name is SMOC. An overview of MBPC was summarized by Qin[31,32], not only theoretical controllers but most of the commercial solutions were analyzed. In 1986 Clarke[8,9,10] presented the GPC or Generalized Predictive Controller. Clarke tried to develop a MBPC unifying all controllers in one algorithm. The way for getting this target is to use a general model, as general as possible, thus he decided to use the CARIMA model, this is a standard ARX plus a derivative white noise. Another proposal was done by Soeterbock[37] in which adaptative controller as general as possible. In 1990 Zafirion [42] exposed the possibility of destabilize the system by including constraints in the optimization process. This fact should be considered, because a stable system and stable controller can be destabilized by mean of including the constraints.
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Galanti[15] proposed, two years later, an interesting solution for constrained by dead zone. But Zheng[43] proposed an intelligent development for solving that problem. It consists on developing soft constraints in place of constraints. The system and controller must be under the constraints, I mean, it is something compulsory. But the soft constraint is not a constraint anymore, this is a recommendation, so id you are out the soft constraint for a while, this is not an alarm, this is a warning. Thus the stability of the system is stronger, this kind of soft constraints are suitable for outout.
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In 1997 Rositer and Kouvaritakis[22] proposed the terminal constraint in order to improve the stability when constraints in MBPC. A similar work was presented in Automatica by Havlena [18] where stability of optimum control when constraints were analyzed. That year some people[36] presented studies about tuning in MBPC for SISO systems.
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Two years later in 1999 Bordons and Camacho[2] summarized the state of the art in their book. They mainly focus the study in a wide development of GPC as a General proposal of MBPC.
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During century XXI MIMO approaches of MBPC were developed. Nuñez[26] proposed Hierarchy control, well accepted by Industry. An additional problem when MIMO MBPC approach is carried out, is instability due to problems in matrix as Pannochia[29] suggested. Poulser and Kouvaritakis[30] exposed some real application in chemical reactor in which MBPC offer its possibilities. Moreover, Kouvaritakis[21] presented different optimization tools in place of quadratic programming.
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An special case in the MBPC is the GPC. Due to special characteristics is one of the most developed controller. It was published in 1987 by Clarke, in these papers the linear controller was proposed[7,8]. Thi first idea was to find a controller with weak computational cost to be used in adaptative applications. In fact, Clarke and Gawthrop[5,6] developed the GMV or controller of minimum variance in 1975 and 1979[5,6]. GPC inherited from GVM the prediction model CARIMA. CARIMA is based in a transfer function plus a white noise derivative model. All previous cotroller use to find some problems:
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Minimum phase zeros process can not be used.
Instable process controlled by open loop architecture.
Robustness when delays or not expected delays.
Robustness when poles zeros were over stimulated.
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Not in all contemporaneous controller appeared that problems, but in most of them. CARIMA model include a derivative model which made this model suitable for real applications. GPC contains the main bases of the MBPC.:
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Prediction model: CARIMA model, what have some interesting proprieties.
Cost Index: with quadratic error in a time slot or prediction horizon plus a control horizon or steps of control action.
Optimization tool: at this point the GPC is using the standard optimization tool, roots in the first derivative and checking the second one is positive.
Mobil horizon: it mean applying the first control action and recalculate every step updating the new information.
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During eighties the team of Clake were developing GPC. In 1988 Tsang[40] in this team explained the importance of using constraints. Once this point, two problems should be solved: include constraint in the optimization and improving stability in close loop.
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In 1992 few paper about robustness in GPC were published. Soeterboek[38] tried to generalize the MBPC in one general book. The importance of T polynomial (noise polynomial poles) were widely explained, specially about robustness of the algorithm. Moreover the correct tuning of lambda parameter (or weight of control actions) in the GPC robustness. That year Kouvaritakis[21] published a paper about GPC Stability and Mosca[25] proposed a control solution about constraints in dead zone. This year Albertos[1] published a new formulation on the model based in Diophantine decomposition.
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In 1994 a paper published by Clarke team is presented. Chow[4], the author of this paper, proposed some constraints applied to GPC which are: slew rate constraint, Coulomb friction, histeresys and dead zone by using two optimization algorithms; quadratic programming and Lagrange multipliers. In the University of Seville was presented by Camacho[3] an empirical approach of GPC. It consisted in using standard linear model (normalized) and setting up different model and different GPC every control area, thus a gain Schedule of GPC was proposed.
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One of the most important problems available in Industrial application is the disturbance. GPC can carry out this problem due to the CARIMA model. In this field Beg in 1995, published an application of controlling a MIMO system by mean of SISO controllers and feed forward architecture.
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In 1996 an approach with heuristic algorithm, genetic algorithms as a optimization tool in GPC scheme. This proposal was developed by CPOH[24], a R+D team in the UPV (Politecnical University of Valencia). In 1997 Corradini[11] proposed as hybrid solution between SMC (Sliding mode control) and GPC, in order to improve the stability.
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During this century the papers in this field are focused in real application if MBPC and GPC, in particular, or Industrial applications. The team of Seville University published a high level GPC MIMO controller and PID controllers in low level, the same was done by Kennel[19]. Salcedo[34] from CPOH, published a paper in which CARIMA model is changed by state space CARIMA model. Thus the algorithm is faster, the computational effort is lower and less storage information is needed, but an state observer must be used.
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In 2002 Perez de la Parte[13] from Seville, tried to improve the robustness by the same way as Corradini. Sanchis[35] used another solution for improving the robustness, he proposed the use of singular values decomposition SVD.
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2.3. Planning
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When MBPC is selected to be the controller for a system, some steps must be done. Firstly the system must be studied. Secondly the model system must be obtained. Thirdly the control must be designed. Fourthly the control should be implemented and tuned.
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The system must be studied. We have to discover the main variables, inputs, outputs and disturbance. Moreover the main dynamics and behavior of the system should be considered.
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Getting the model of the system should be done. The model is one of the key in the MBPC. Getting a good quality model is sometimes the difference between success and failure.
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After that, control architecture and design should be done. Finally tuning the algorithm and checking parameters as robustness or others must be done.
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3. Model based predictive controller
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In this section the control algorithms will be developed. MBPC are based in 4 different beams. One of the most important is the system model. Other important tool is the optimization process.
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Many time the system has a, more or less, linear behaviour. At this time a linear model uses to be a good approach. Linea model has the main advantages of the small computational cost. Specially when constraint are not considered. The optimization can be carried out off-line. Thus the control action is a combination of multiplications and additions. So the computational effort into a microcontroller is very small.
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Sometimes the system has a nonlinear behaviour. Then linear approach can not be carried out by its selves. A non-linear model with constraints use to cost the highest computational effort, but with a high accurate control behaviour.
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Sometimes linear approaches in gaps use to be an intelligent solution. It consists on using different linear models around linear system behaviour, and changing the model when the system is out of its gap of influence of that model. Thus an offline optimization can be carried out and the computational effort, during the control action, is very low. It looks like gain schedule but, in place of PID parameters, GPC controllers are used.
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Thus a standard GPC controller is not going to be developed because it can be obtained by simplifying the GPC with disturbance measurement.
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The system use to be influenced by some parameters. Those parameters many times could be considered as measurable disturbance. A measurable disturbance is an input of the system that is not controlled by the control algorithm. For instance, in a Diesel engine, the air management loop control VNT (variable nozzle turbine) and the EGR system. Mass fuel injected is controlled by the user. But the mass fuel injected in the engine affects quite a lot in the air management behaviour. Thus this important input has to be considered as measurable disturbance.
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These kinds of inputs need a special management in the algorithm. So this chapter shows one way to consider them into the algorithm, for past disturbance and also for future disturbances. Two different algorithms will be developed with this approach, the one based in the linear Markov parameters and the GPC (Generalized Predictive Controller) developed by Clarke.
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3.1. GPCDM, algorithm development
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We now briefly describe the control algorithm which is key issues of this paper. The control algorithm was based on the model shown in the Figure 1 in which there is the CARIMA model plus an input p which is a measurable disturbance. To simplify the development one input and one output are considered.
Equation (1) is the discrete one of the model shown in figure X. The yu is the output behaviour depending on the direct input. yp is output behaviour from measurable disturbance and n is the behaviour depending on few things like non-linearity, others inputs etc.
n(k+i|k) is the n prediction of (k+i) with the information in k sample time. Diophantine decomposition is applied in order to get the system response due to pass inputs and future inputs in different terms. Moreover the mean of ξ is zero, then:
Where: y is the predicted system output vector; model matrices are represented by G, Γ, ΓP, GP, and F (G and GP represent the future response; and Γ, ΓP and F represent the past system response or natural response); Δ is the incremental operator (1-z\n\t\t\t\t\t\n\t\t\t\t\t\t-1\n\t\t\t\t\t\n\t\t\t\t\t); u is the vector of future control action increment; p is the disturbance increment vector; the past action control filtered by 1/T is u\n\t\t\t\t\t\n\t\t\t\t\t\tf\n\t\t\t\t\t ; the past disturbance filtered by 1/T is p\n\t\t\t\t\t\n\t\t\t\t\t\tf\n\t\t\t\t\t ; the past system output filtered by 1/T is y\n\t\t\t\t\t\n\t\t\t\t\t\tf\n\t\t\t\t\t.
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Equation (8) is the system model in a matrix form. More over we need the control cost which depend on error cost and control effort, represented in (9) as a matrix equation. Where y\n\t\t\t\t\t\n\t\t\t\t\t\tsp\n\t\t\t\t\t is a vector of future set points, λ is a diagonal matrix of control effort factor and α is the diagonal matrix of error cost.
It is also possible to calculate the first value of equation (11) and using it like a combination of discrete time transfer functions. More details can be studied in Garcia-Ortiz[17] where the algorithm is widely explained and Camacho & Bordons[3].
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There are other ways to reach the same result. In terms of model in figure 1, may be considers ad a MISO system (Multiple input single output). Getting ecuations from MIMO CARIMA developed models we can find:
Thus G\n\t\t\t\t\t\n\t\t\t\t\t\t11\n\t\t\t\t\t is the G in equation (8) and G\n\t\t\t\t\t\n\t\t\t\t\t\t12\n\t\t\t\t\t is GP in equation (8). So is very easy to see that both ways get the same result.
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3.2. DMCDM, algorithms development
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A similar approach can be developed for DMC controller. The differences are that prediction model is based in Markov coefficients.
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Figure 2.
Box Jenkins model. Model based for impulse response with measurable disturbances.
Where g0 = 0 due to the delay in the discrete systems and gN = 0. From gN to gN+M=0 whatever M. from this point all systems controlled by DMC should be type 0. All systems must be stable and integer term is not allowed. Most of the industrial processes have these characteristic, which is why this controller have been developed widely in the industrial applications.
So during the development both models will be used, depending the interest every time. Additionally some information will be defined:
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P: prediction horizon. Number of time steps will be considered in the cost index and the optimization process.
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M: Control Horizon. How many control actions are allowed in the optimizer to reach the target. From M to the future no movements in the control action are considered.
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N: number of coefficients with information in the impulse response. From N to the future g coefficient is zero.
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All that information will be used in the control cost(16).
Where y(k+i|k) is the value of the prediction for the step k+i, calculated in the instant k with the information available in that time k. ysp(k+i) is the set point in the step k+i (when future set point is not available, this variable is considered as the set point in k. Ψ2 is the weight of error cost and R2,i+1 is the weight of the control effort. Δu(k+i) is the increment of the control action in time=k+i.
Thus we are ready to get the prediction model in the DMC. The main target is getting the future response of the system from one step time and the behaviour if the system when future control actions will be applied. The model will contain the system natural response, the one due to the past control actions applied in the system (this one is not changeable, because is produced) and the forced response, the one due to future control action (this one can be different because will be decided by the controller).
\n\t\t\t\t
Once the bases are fixed we will start with the model development, thus we will get the system response in a generic step k+t, from the information available in k.
Where \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tL\n\t\t\t\t\t\t\t\t\tP\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t=\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t⋯\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tP\n\t\t\t\t\t\t\t\t\t\tx\n\t\t\t\t\t\t\t\t\t\t1\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tT\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t and:
At this time we are able to get future response from the data available in the step k, but we have to include system information every step time so: d(k) = yreal(k) - y(k), this is the difference between real system and prediction.
\n\t\t\t\t
Finally yf is an array of output system predictions. Δuf is an array of future increment control actions. Δup is an array of past control actions. Sf is a matrix with dynamic future system response or forced response matrix and Sp is a matrix with past dynamic system response or natural response matrix.
Thus H is the same as (H Matrix) but, intead of Sf is Sfs.
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Every step time the controller is applying the first control action and update the data for getting new optimal control every step time. Thus only the first line in the matrix equation should be calculated. Additionally this matrix equation, first line, can be presented as a Z transform function transfer. This is the application of the fourth basis of MBPC, receding horizon policy. It means that every step time a new control action will be calculated by means of updating data in the algorithm. Thus the control algorithm must be presented as:
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In which h(z) is the first row of the H Matrix. The vector h(z) has different coefficients which will be multiplied by future set point, if the information is available. If the future set point is not available, a constant is calculated by adding all the h(z) vector coefficients.
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D(z1) polynomial in which the coefficients are the result by multiplying h(z)·Sp. This array is in z-1, because is using past information. All in all the algorithms structure is shown in figure 5.3.
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Figure 3.
DMCDM architecture with receding horizon.
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When I(z-1) is the convolution of two vectors: one array is product of h(z)·Spp, the second array is Δ operator. Spp is new matrix calculated as Sp but using Markov coefficients of disturbance of the system. More information is available at Thesis of Garcia[16].
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4. Practical set up and experimental behavior
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This chapter is presenting the author’s experience for tuning the algorithm. Also the steps recommended for carry out a control solution. MBPC is a family on controller very suitable for controlling systems in different conditions. They can consider constraints, control cost, future information, model behavior etc... Getting the model by linearization should be considered. There are many identification algorithms, using the right ones and using the best parameters can bring you success or failure.
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4.1. System identification
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In control engineering ther are two important concepts that are opposite: Robustness and Performance. Usually as much Robust is a controller as less Performance it offers you. So as much Performance we demand as much easy to reach instable zones. There is one way to get both characteristic at the same time, Model accuracy. If we have a better model or a more accurate model, we will be able to demand more performance and more Robustness. So the system model is one of the most important topics in control engineering.
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The most famous PID control tuning techniques are Ziegler-Nochols rules or Cohen-Coon method. Methods have a main characteristic, you can tune a PID controller with Robustness, but unfortunately performance is not good enough. The main reason for is that you have not a system model. So the quality model is very poor, thus Robustness is more important than performance. When Performance is important, model quality must be improved. As good is the model as good will be the control.
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By other hand, the easiest way to get a model is by mean of physical equations. If we know the physics of the system, by using physical equation we can obtain a very good quality model. When physical equations are used model reach an important complexity, sometimes the computational effort done by the microcontroller or commuter is very high. Some models need mode than two day for calculating few second of real physical behavior.
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An important approach use to be linear model. The computational cost of linear model, use to be very low (few micro seconds every step time). Additionally much real system use to have a linear behavior, so linear approach, sometimes, is the most intelligent solution. When system has a non-linear behavior other solutions can be approached. Linearization is a good solution for that.
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When physical equations are hard to compute, linearization of them bring us a model easy to carry out and fast to be calculated. Another solution is linear model by identification techniques. When identification is our choice this kind of model works very well in the identified point and around of it, but we have problems when the operating point is too far from the identification point. When this problem appears a new linear model can be identified. We can do this topic as many times as we need. Thus we apply a technique similar to gain schedule but with models.
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The main advantages of liner models are:
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It is a simpler, so you have an easier solution for solving Differential equations.
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The system behavior can be observed.
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Any kind of order can be used. We should fit the order to the one system expected.
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As disadvantages we can find:
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The solution is exact, only in the operating pint and good enough close to it, but it is not good as far as we are.
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Experimental data is needed and sometimes particular experiments are not possible to carry out.
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No physical sense in poles and zeros identified can be found.
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Math of the transfer functions, state space model and other can be studied in Ogata continuous, Ogata Discrete or Zhu [43] books. Additionally some identification algorithms were tested and studied widely in bibliography.
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Lineal model available in bibliography:
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Markov coefficients or impulse response model, there is an equivalent model, the step response.
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ARX: autoregressive with exogenous variable. ARMAX: Autoregressive moving average with exogenous variable. OE: Output error. BJ: box Jenkins. CARIMA or ARIMAX: autoregressive integral moving average with exogenous variable. State space models deterministic, advanced and stochastic ones etc. all models are descried in Garcia [17].
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Finally the solution proposed is linear models by sections. When linear model is valid, then it shall be used, when linear model is not valid a new linear model must be placed.
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4.1.1. Experimental data for identification
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Many times data available is not a suitable data for identification. That is why system is working and particular experiments are not allowed. Thus a measurement of system behavior should be done and transient data must be used in the identification. Steady data does not include important dynamic information, so transient contains the system dynamics.
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When particular experiments can be done, they must be well chosen. An easy experiment use to be a step applied to the system. The step should contain some characteristics: powerful enough, long enough, the response must be in the linear zone. Theoretically white noise is the best signal. But some authors, as Luo[22] or Zhu[43], studied physical inputs and pseudo aleatori input is the best choice.
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Some times a simple step or impulse can be applied and Strejc, Csypkin methods can be applied. Simple model is obtained and some time it is good enough. Frequency identification methods are also available, based in bode diagram or graphical techniques.
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Identification algorithms are proposed in this chapter. Experimental data must be pre processed and model structure should be well chosen. Parametrical and state space identification can be carried out.
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When parametrical identification is proposed the following steps should be respected:
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Experimental identification design and data acquisition.
Data pre processed: filtering, remove data tends, data normalization etc.
definition of model structure.
Identify the model by using an appropriate identification algorithm.
Studying model proprieties (zeros, poles, gain, … ) and model validation.
If the model is the expected one, this task is finished, else come back to step 1, 2 or 3, depending on problems detected.
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4.1.2. Identification algorithms
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The identification algorithms proposed and tested in this work is the PEM (Prediction error method). Other where tested N4SID (Numerical Algorithm for Sub Space State System Identification) widely explained in [108,141,150]. Bud PEM is preferred by this author with very well results. PEM is an identification algorithm of state space model, so this model should be converted in a equivalent transfer function.
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4.1.3. System identification
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A Diesel engine is identified. Some experimental data is shown. At this studied case air path or air management system is going to be controlled. If suitable linear models cannot be found, algorithms based on the Hamertein and Wiener models [12] can be used or non linear models. The disadvantages of this latter group are: high computational cost and the difficulty of programming them in a commercial ECU (electronic control unite).
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The system air-path in a Diesel engine consists on controlling VNT (variable nozzle turbine) in order to keep boost pressure in the set point. The engine controlled is a Diesel engine of heavy duty applications, see figure 4. This is a truck engine, where EGR system is not included but VNT is available. Air-path is influenced by mass fuel injected, as much fuel injected into cylinder, more power available in the turbine and more boost pressure. Fuel injected is available information in the ECU.
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Figure 4.
System controlled.
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Unfortunately the fuel injected is selected by user’s needs. Thus fuel injected can not be controlled by control algorithms and it is determined as a measurable disturbance.
Identification test performed at 1200 rpm. PWM signal is the control action on the VNT, and mf is the injected fuel. Important variations in the engine speed are due to the dynamometer response.
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The VNT input applied can be seen in figure 6, where pseudo-random PWM (Pulse Width Modulation PWM) is applied in the VNT. Aleatori fuel is injected in the engine. With these inputs the boost pressure response is shown in figure below:
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Figure 6.
Boost pressure response.
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Apparently these data have not any kind of relation, but continuing with identification process we can obtain a system model. As shown the model poles are two. Physical systems don’t use to have more than three poles, when model is bigger than this we could find some problems, identification algorithm tried to identify signal noise, and it has identified fast poles.
Checking the results, we could realize that the system model is good enough, see figure 7.
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Figure 7.
Measured evolution (black) and predicted evolution (grey) of the boost pressure for the validation test at 1200 rpm.
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4.2. Control system examples
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Once the model is identified the controller is calculated by using control algorithms developed in section 3, it is time to control the system and testing with experimental data. A comparison is going to be shown in the following figures. The test proposed is a transient from steady conditions to full load. This is a typical situation in which a Diesel engine is requested full load to make advancement in a road. The engine is turning at 1200 rpm and no torque is need, suddenly all torque available is needed, and then engine, electronics and thermodynamics must change to the new conditions. Four different algorithms are controlling the system. The idea is testing those algorithms in order to check the engine behavior if the controller of air path is one algorithm or other one.
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The algorithms tested were: PID controller with feed forward of fuel behavior, PID with Fuzzy login in parameters plus feed forward in fuel injected, GPCDM developed before and DMCDM shown in section 3.
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PID controller was tuned for getting the best feasible performance. It was tuned from Ziegler Nichols parameter to updating PID until the performance was good enough. Feed forward was programmed to use measurable disturbance behavior available.
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PID Fuzzy: this controller was programmed as one improvement of standard PID. The proposed algorithm is a gain schedule depending on error. Feed forward is also used here.
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DMCDM: a model predictive controller based on impulse response with measurable disturbance. Control horizon and prediction horizon are chosen as long time. When performance is required long control horizon and prediction horizon must be chosen. Fro tuning the algorithm one weight is fixed to 1 and the other is changed.
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GPCDM: this is an other MBPC, so many ideas explained for DMCDM are applicable here. Long time control horizon and long time prediction horizon are used for reaching good performance. The real control cost is not available, so we keep one weight fixed and we fit the second weight for tuning the system. GPC and GPCDM have a very special parameter, which is not in other kinds of MBPC. T polynomial is a parameter, which theoretically is a polynomial with zeros of the colored derivative noise from CARIMA model. In fact, it is a parameter for tuning the GPC. When noise is present in the system, this parameter must be tuned else you could put it as 1. there are many works explaining the behavior of the parameter Clarke [26]. Moreover the robustness of the system is widely improved by tuning the parameter [28,132]. Typical values of T polynomial are T= 1-0,7*z-1; or T = convolution of ( [1-0,7.z-1],[ 1-0,7.z-1]), sometimes 0,7 are placed by 0,8 or 0,6. This is an empirical rule that can help the designer to choose the best option.
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The structures are programmed as follow:
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PID feed forward:
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The boost pressure set point is depending on engine speed and mass fuel requested by used. Additionally the fuel injected is the measurable system disturbance. So the fuel injected is the feed forward control action. Error between boost pressure set point and measured boot pressure is the input to the PID controller. Control action calculated is the addition between feed forward and PID in figure 8. Finally control action applied is the calculated one processed by one anti-windup. Experimental result will be shown in following figures.
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The controller proposed and programmed in the figure 9 is similar architecture to the PID proposed but some differences. The PID parameters are scheduled by a Fuzzy logic technique. Other sub-systems are the same, PID, feed forward and anti-windup. The behavior is similar to the PID but parameters can be tuned more accurate than a standard PID controller. The main difficult in this controller is the tuning process, because theparameters are more difficult to set up. The experience of the designer must be higher than the standard PID. For tuning the controller many experiments must be done and widely analyzed for choosing the best option.
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Figure 8.
PID Architecture programmed.
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Figure 9.
PID scheduled by fuzzy controller and feed forward.
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GPCDM architecture is the one proposed in the figure 10. Set point is processed by h(z). when future set point is not available, future ones are the actual one. Thus h(z) became as a constant term. Additionally measurable disturbance can be processed as past information
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Figure 10.
GPCDM programmed.
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and future information, see equation (30). In this experiment only actual and past fuel injected can be processed, but future disturbances can be considered. Anti-windup is also programmed, when control action calculated is higher than maximum, the algorithm is frozen. Thus the integral component of the algorithm can not affect to the algorithms performance.
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Figure 11.
DMCDM programmed.
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DMCDM programmed is shown in figure 11. Measurable disturbances are considered and included in the controller. Anti-windup is programmed, a similar system to the one used in the GPCDM. When maximum or minimum control action is reached, the algorithm is frozen until control action is going to the opposite way.
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5. Experimental results
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As explained before full load transient test were done. The engine was in steady conditions or idle conditions at 1200 rpm. When test starts, full load is required from the engine. Thus maximum quantity of fuel is injected in order to burn it and getting the power. Additionally air mass is needed for burning the fuel, which is why VNT must be controller to give the boost pressure necessary for the new conditions. More boost pressure that necessary produces an increment in exhaust pressure and some loses of torque and power and more pollution in exhaust gases. If we have less boost pressure than set point then we will not have enough air flow for burning the fuel in the right conditions.
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Many experiments were done, but only the best performance is shown in this chapter. In figure 12, boost pressure can be observed, figure 13 contains exhaust pressure behavior, and the figure 14 shows engine torque.
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Figure 12.
Boost pressure evolution in transient conditions.
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Figure 13.
Exhaust pressure evolution in transient conditions.
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In figure 12, boost pressure behavior is presented. Thin line is the set point requested from idle condition to full load one. This is the best boost pressure conditions considering, efficiency, torque, power, pollution etc. so the system must be in set point as soon as possible. Continuous lines are PID controller, solid red line is the PID with feed forward and solid blue line is the PID Fuzy logic controller. GPCDM is the dashed blue line and DMCDM is the dashed black line.
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When transient test starts, the first part of the test are the same in all controllers. This happens because VNT is in bounds, completely close and no different performance can be appreciated. When VNT must be opened controller performance is different. GPCDM is opening the VNT before than others, that is why over oscillation is lower than others. The predictive model CARIMA helps the GPCDM to advance the system behavior, thus GPCDM can predict the VNT open to be before and avoiding the over oscillation. PID must be tuned with low influences of derivative term, the reason is the stability. Derivative term can produce instable conditions in the system. This controller realizes later about over oscillation and start opening the VNT later. The worst performance is done by DMCDM. Maybe the system model is very single and it is more sensible to non-linearity. Fuzzy controller improves the PID behavior because it is an improvement of PID.
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\n\t\t\t\tFigure 12. contains the exhaust boost pressure. The steady conditions are reached in similar time to boost pressure. The over-pressure in exhaust produces over-speed in turbine or torque loses, and more pollutants than permitted.
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Figure 14.
Torque evolution and air-path influence during transient.
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\n\t\t\t\tFigure 14. shows torque performance. Both GPCDM and Fuzzy reach steady conditions at the same time. So the torque is not very influenced by boost pressure. But if other controllers are analyzed engine torque is influenced and oscillations are very uncomfortable.
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Finally a summary of system response are shown in table 1 and table 2. Table 1 summarizes the system response from the point of view of engine analysis. Table 2 analyzes the system from the point of view of control parameters.
The most important variable from the point of view of control is the control Cost or J. This variable summarized the control behavior from the point of view of control cost, considering control effort and control error.
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5.2. Discussion
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The first conclusion that can be drawn from analyzing the results is that when fuel mass is considered in the algorithm, behavior is always improved. This fact had been observed during the identification process and is logical, since the more information the algorithm has about the system, the better control it can apply. This is due to the air management process being more influenced by fuel amount than VGT control actions. The fuel amount gives energy, this energy goes to the turbine and the turbocharger increases its speed, thus the air management system changes. The delivered fuel quantity therefore has a very strong influence on the air management system. If this variable is included in control algorithms, better control will be obtained.
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The analysis of the behavior of the Fuzzy controller seems to show quite a good response but at the cost of a high control effort. One of the disadvantages of this type of controller is the high overshoot of the exhaust pressure. The control effort is one order of magnitude higher than the baseline controller. However, the greatest disadvantage is their complicated tuning process due to having a large number of independent parameters.
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Another interesting aspect is that not all the MPCs work better than the existing controllers. For example, the DMC provided rather doubtful results. This was a surprise and could be explained as follows: the prediction model of the DMC is considered to be highly sensitive to nonlinearities in the system, since the system itself is highly nonlinear (in spite of having linear zone models). This algorithm finds it difficult to predict engine behavior and therefore does not take the right decisions. However, the predictive control GPC algorithm is based on the CARIMA model and is more robust to system nonlinearities. In fact, one of the most important parameters for the correct GPC tuning was the polynomial T(z).
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The best all-round algorithm was the GPCDM. The most significant difference between algorithms can be seen in the exhaust pressure in Figures 8 and 9. This parameter indicates the effort made by the engine to control the air path and high pressure has a negative influence on engine torque.
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The air management system, and consequently various engine parameters, was strongly influenced by the controller used. One of the most susceptible to this influence is PMEP, which also affects engine torque, turbocharger pressure, etc. If an overshoot occurs it will produce exhaust pressure peak and pumping losses. This fact produced less efficiency and torque during transient conditions.
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6. Conclusions
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Few linear models were obtained from the air management system of the engine and they are quiet good approximations of the nonlinear plant. These models can be used for linear controller design with low computational effort. GPCDM, DMCDM, PID and Fuzzy PID are tested in a standard test bench. In comparison to standard gain scheduled PID, setpoint tracking could be improved. The additional degrees of freedom of GPC can be used for tuning the robustness of control.
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Acknowledgments
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I would like to thank Universidad Jaume I from Castellon (Spain). I am assistant professor in the engineering department. Moreover I want strongly thank Vinci Energia. In this company Ia m working as a professional researcher and many of the improvements in control theory are applied to solar tracking system in my actual investigations. And of course, this work was impossible to do without CMT thermal engines of the “Universidad Politécnica de Valencia”.
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\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/16071.pdf",chapterXML:"https://mts.intechopen.com/source/xml/16071.xml",downloadPdfUrl:"/chapter/pdf-download/16071",previewPdfUrl:"/chapter/pdf-preview/16071",totalDownloads:2318,totalViews:186,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:28,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"October 27th 2010",dateReviewed:"February 17th 2011",datePrePublished:null,datePublished:"July 5th 2011",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/16071",risUrl:"/chapter/ris/16071",book:{id:"160",slug:"advanced-model-predictive-control"},signatures:"Jose Vicente Garcia-Ortiz",authors:[{id:"31373",title:"Dr.",name:"Jose Vte.",middleName:null,surname:"Garcia-Ortiz",fullName:"Jose Vte. Garcia-Ortiz",slug:"jose-vte.-garcia-ortiz",email:"jortiz@emc.uji.es",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. State of art",level:"1"},{id:"sec_2_2",title:"2.1. Control engineering",level:"2"},{id:"sec_3_2",title:"2.2. Control algorithms MBPC history",level:"2"},{id:"sec_4_2",title:"2.3. Planning",level:"2"},{id:"sec_6",title:"3. Model based predictive controller",level:"1"},{id:"sec_6_2",title:"3.1. GPCDM, algorithm development",level:"2"},{id:"sec_7_2",title:"3.2. DMCDM, algorithms development",level:"2"},{id:"sec_9",title:"4. Practical set up and experimental behavior",level:"1"},{id:"sec_9_2",title:"4.1. System identification",level:"2"},{id:"sec_9_3",title:"4.1.1. Experimental data for identification",level:"3"},{id:"sec_10_3",title:"4.1.2. Identification algorithms",level:"3"},{id:"sec_11_3",title:"4.1.3. System identification",level:"3"},{id:"sec_13_2",title:"4.2. Control system examples",level:"2"},{id:"sec_15",title:"5. Experimental results",level:"1"},{id:"sec_15_2",title:"5.2. Discussion",level:"2"},{id:"sec_17",title:"6. Conclusions",level:"1"},{id:"sec_18",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAlbertos\n\t\t\t\t\t\t\tP. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tOrtega\n\t\t\t\t\t\t\tR. “.\n\t\t\t\t\t\t\n\t\t\t\t\t “On generalized predictive control two alternative formulations”. Automatica, 25\n\t\t\t\t\t255\n\t\t\t\t\t5\n\t\t\t\t\t753\n\t\t\t\t\t755 , 1989.\n\t\t\t'},{id:"B2",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBordons\n\t\t\t\t\t\t\tC. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCamacho\n\t\t\t\t\t\t\tE. F. “. 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C.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tcontroller”\n\t\t\t\t\t\t\tI. F. A.\n\t\t\t\t\t\t\n\t\t\t\t\t IFAC world congress, San Francisco USA, 1996\n\t\t\t\t\n\t\t\t'},{id:"B25",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMosca\n\t\t\t\t\t\t\tE. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZhang\n\t\t\t\t\t\t\tJ. “.\n\t\t\t\t\t\t\n\t\t\t\t\t “Stable redesign of predictive control”. Automatica, 28\n\t\t\t\t\t28\n\t\t\t\t\t1229\n\t\t\t\t\t1233 , 1992.\n\t\t\t'},{id:"B26",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNuñez\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCueil\n\t\t\t\t\t\t\tJ. R. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBordons\n\t\t\t\t\t\t\tC. “.\n\t\t\t\t\t\t\n\t\t\t\t\t “Modelado y control de una almazara de extraccion de aceite de oliva”. XXII Jornadas de Autimatica, 2001\n\t\t\t\t\n\t\t\t'},{id:"B27",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNyquist\n\t\t\t\t\t\t\tH. “.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRegeneration\n\t\t\t\t\t\t\ttheory”.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBell\n\t\t\t\t\t\t\tsystem.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\ttech\n\t\t\t\t\t\t\tJournal.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tVol\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t 11, 1932\n\t\t\t\t\n\t\t\t'},{id:"B28",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tOverschee\n\t\t\t\t\t\t\tP.\n\t\t\t\t\t\t\n\t\t\t\t\t Van y Moor B. De. 4 Subspace algorithm for the identification of combined deterministic-stochastic systems”. Automatica, 30\n\t\t\t\t\t75\n\t\t\t\t\t93 , 1994.\n\t\t\t'},{id:"B29",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPannocchia\n\t\t\t\t\t\t\tG. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRawlings\n\t\t\t\t\t\t\tJ. B. “.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRobustness\n\t\t\t\t\t\t\tof. M. P. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tDisturbance\n\t\t\t\t\t\t\tModels.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tfor\n\t\t\t\t\t\t\tMultivariable.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tIll-conditioned\n\t\t\t\t\t\t\tProcesses”.\n\t\t\t\t\t\t\n\t\t\t\t\t Technical report TWMCC 2001-02\n\t\t\t\t\t2001\n\t\t\t\t\t02 .\n\t\t\t'},{id:"B30",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPoulser\n\t\t\t\t\t\t\tN. K.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKuovaritakis\n\t\t\t\t\t\t\tB. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCannon\n\t\t\t\t\t\t\tM. “.\n\t\t\t\t\t\t\n\t\t\t\t\t “Constrained predictive control and its application to a coupled-tanks apparatus”. Int. 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Automatica, 14\n\t\t\t\t\t14\n\t\t\t\t\t413\n\t\t\t\t\t428 , 1978.\n\t\t\t'},{id:"B34",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSalcedo\n\t\t\t\t\t\t\tJ. V.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMartinez\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSanchis\n\t\t\t\t\t\t\tJ. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBlasco\n\t\t\t\t\t\t\tX. “.\n\t\t\t\t\t\t\n\t\t\t\t\t “Design of GPC’s in state space”. Automatika, 42\n\t\t\t\t\t4234\n\t\t\t\t\t159\n\t\t\t\t\t167 , 2001.\n\t\t\t'},{id:"B35",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSanchis\n\t\t\t\t\t\t\tJ.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMartinez\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tRamos\n\t\t\t\t\t\t\tC. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSalcedo\n\t\t\t\t\t\t\tJ. V. “.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tPrincipal\n\t\t\t\t\t\t\tcomponent. G. P. C.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\twith\n\t\t\t\t\t\t\tterminal.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tequality\n\t\t\t\t\t\t\tconstraint”.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t15 15th IFAC World Congress, Barcelona (Spain), July 2002.\n\t\t\t'},{id:"B36",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tShridhar\n\t\t\t\t\t\t\tR. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tCooper\n\t\t\t\t\t\t\tD. J. “. A.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\ttuning\n\t\t\t\t\t\t\tstrategy.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tfor\n\t\t\t\t\t\t\tunconstrained. S. I. S. O.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tmodel\n\t\t\t\t\t\t\tpredictive.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tcontrol”\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t Ind. Eng. Chem Res., 36\n\t\t\t\t\t36\n\t\t\t\t\t729\n\t\t\t\t\t746 , 1997.\n\t\t\t'},{id:"B37",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSoeterboek\n\t\t\t\t\t\t\tA. R. 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Eng., 14\n\t\t\t\t\t14\n\t\t\t\t\t359\n\t\t\t\t\t371 , 1990.\n\t\t\t'},{id:"B43",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZheng\n\t\t\t\t\t\t\tA. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMorari\n\t\t\t\t\t\t\tM. “.\n\t\t\t\t\t\t\n\t\t\t\t\t “Stability of model predictive control with soft restraints”. Proceedings 33 CDC Florida, 1018\n\t\t\t\t\t1023 , 1994.\n\t\t\t'},{id:"B44",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZhu\n\t\t\t\t\t\t\tY. y.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tBackx\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tIdentification of multivariable industrial process, for simulation, diagnosis and control. Springer-verlag, 3-540-19835-0\n\t\t\t\t\t1993\n\t\t\t\t\n\t\t\t'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Jose Vicente García-Ortiz",address:null,affiliation:'
University Jaume I and Vinci Energía, Spain
'}],corrections:null},book:{id:"160",type:"book",title:"Advanced Model Predictive Control",subtitle:null,fullTitle:"Advanced Model Predictive Control",slug:"advanced-model-predictive-control",publishedDate:"July 5th 2011",bookSignature:"Tao Zheng",coverURL:"https://cdn.intechopen.com/books/images_new/160.jpg",licenceType:"CC BY-NC-SA 3.0",editedByType:"Edited by",isbn:null,printIsbn:"978-953-307-298-2",pdfIsbn:"978-953-51-6015-1",reviewType:"peer-reviewed",numberOfWosCitations:41,isAvailableForWebshopOrdering:!0,editors:[{id:"10515",title:"Prof.",name:"Tao",middleName:null,surname:"Zheng",slug:"tao-zheng",fullName:"Tao Zheng"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"723"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},chapters:[{id:"16055",type:"chapter",title:"Fast Model Predictive Control and its Application 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the Formation of Adult Language Skills on the Example of English",doi:"10.5772/intechopen.96926",slug:"concept-of-a-management-system-for-the-formation-of-adult-language-skills-on-the-example-of-english",body:'
1. Introduction
English dominates the world as the language of science, international communication, and information technology. The need for ubiquitous English language teaching continues unabated, and over time, everything gets stronger. Education systems being one of the most conservative systems of society do not keep up with scientific and technological progress and social changes do not have the capabilities and resources to solve the problems of quickly preparing a large number of adults for language, social and industrial activities. In addition, the coronavirus epidemic has befallen the whole world has aggravated all social problems many times over. We live in a time when the main state structures simply do not cope well with the duties assigned to them:
health care systems fail to perform their functions and medical staff are forced to show real heroism to rescue people;
education systems are not able to train people in professional and language skills in a sufficiently short time as a result of which the migration crisis turns into a catastrophe and this is becoming especially relevant now during the pandemic.
The transition to remote learning is a forced measure, but the distance education currently practiced cannot show decent results primarily due to the tendency to use the same approaches for both classroom and distance learning. The main task today is the transition from distance education to full-fledged e-education. However, we cannot use traditional educational technologies that will not bring the desired effect, but will only undermine faith in new approaches. Therefore, it is necessary to develop such teaching methods that will most fully meet the emerging educational needs and will form the basis of electronic educational systems.
Existing learning management systems (LMS) are mainly aimed at managing standard educational processes for the transfer of theoretical knowledge to the education system and are not suitable for monitoring and managing the acquisition of primary language and labor skills. In addition, they usually have a rather complex interface both on the client side and on the part of the administrator and the creator of the training content. These features almost completely exclude the possibility of using LMS as a tool for self-study at home, for example, in a typical situation of migrants and refugees. And filling such a training system with content for different countries, languages, activities and professions is a task of great complexity, which requires too much time and resources to solve.
Therefore, it is necessary to create a new generation of tools that provide not only the structuring and transfer of knowledge from teacher to student, but also quantitative and qualitative management of the level of skills and competencies of the student and the parameters of his real progress along the learning curve. This is especially important in the field of adult education and retraining. As it was shown in the works of Bandura [1], learning occurs in the process of interaction of the influence of the external environment, human behavior and the properties of his personality. At the same time, the presence of previous experience leads to the fact instead of the usual logarithmic growth, the learning curve acquires characteristic curves with several minima the so-called “barrier to overcome” (Figure 1).
Figure 1.
Comparison of learning curves for retraining.
The use of modern information and communication technologies (ICTs) together with the use of effective models of skills acquisition will reduce and even completely eliminate the impact of such barriers and, accordingly, accelerate learning and increase its success. System analysis and visual modeling tools will help ensure such integration. At the moment, there are no technical limitations for creating a new generation of systems, and only the complexity of combining knowledge, theories and technologies from completely different branches of science, industry and education in one project does not allow us to combine the existing technical solutions that will be proposed in this project in a new way.
2. Prerequisites for creating a system for acquiring language skills
The ability to communicate in a foreign language is most quickly achieved through progressive mastery of labor skills while simultaneously developing the entire language system, which very accurately and in a timely manner implements the speech skills of a new language necessary for the student in his daily activities. This is the only way to quickly form thinking in a foreign language, in which the presentation of thoughts takes place in accordance with the norms of the new language system acquired for its expression in everyday speech. Modern research is interdisciplinary and is located at the intersection of systems analysis, ICT, psychology, linguistics and activity theory.
The relevance of the topic is determined by the need to create a new detailed technology for the formation of speech skills in the electronic management system for teaching a foreign language to adults. In the third decade of the XXI century, both metdological and technological prerequisites have matured for the realization of this goal. Technologies and tools created under their influence are directly influenced by a changing culture, and their further use is a way of accumulating and transmitting social knowledge.
Technologies using Big Data, artificial intelligence, machine learning, speech recognition and synthesis systems, as well as augmented or virtual reality create the technological basis that forms the basis for creating new LMS. In turn, the Korzybsky structural differential, the theory of activity of Leontiev and his followers, the system of interval repetitions based on the ideas of Ebbinghaus, the theory of formation of Bandura skills and the Structural-Visual Method (SVM) form the core of the Electronic language Acquisition Management System (e-AMS).
2.1 Typical systems for teaching a foreign language
There are a huge number of offers on the market to solve the problem of managing foreign language teaching. These are general-type LMS, programs and applications for learning languages, both for individual aspects of the language and for the entire language as a system. In the course of research, a large number of such programs, applications and services were studied and tested. Let’s focus only on the general features of entire classes of programs and services, mentioning only the most typical examples.
LMS of general type, such as Moodle, Docebo, Edmodo, Schoology and many others, are created mainly for the organization of the educational process within the framework of the classical path of knowledge acquisition. They are designed to facilitate and simplify the activities of the teacher, methodologist, and management of the educational institution and contain a large number of properties, functions, and tools. They are difficult to set up and master and are designed for use within large organizations (schools, universities, large firms). They perform their functions well, but it is extremely problematic to adapt them to manage the individual process of obtaining a specific skill.
Specialized programs due to the low methodological competence of programmers in most cases are created for the study of words. The use of interval repetition methods in such programs and services as Memrise, Mnemosyne, Anki, Supermemo, etc. allows you to quickly memorize poorly structured information, which provokes users to spend a lot of time on rather unproductive activities for memorizing the translation of words out of context and structure. This is also the sin of most services and social networks for language classes such as Duolingo, Busuu, LinguaLeo, etc.
The closest prototype is programs for complex language learning such as Rosetta Stone, Tell Me More, Babbel, etc. They offer tools for developing most language skills and are designed more for self-directed language learning. These programs do a good job with their functions, due to the direct, untranslated method laid down in their basis. But they have their own limitations and shortcomings that slow down progress and force people to quit classes. The main one is the same direct method, which reduces exercises to repeated repetition without understanding the logic of the language.
The situation is aggravated by the complexity and congestion of the interface, the non-obvious methods of training and the rather high cost of such programs. Such programs are created by programmers for people who are good at computers and are poorly adapted for use by ordinary people who are not familiar with computer technology. In addition, the presence or absence of a native language in the interface and content of the program does not guarantee the absence of translation, thinking and comprehension of the material in the native language, especially for people who have already had many years of experience in translation training.
To achieve direct understanding and completely untranslated thinking is possible only by combining the ideal balancing of the complexity and pace of training with the abilities and capabilities of the student by controlling the learning curve, which is one of the most important tasks of the controlled formation of language skills. As the review showed, at the moment there are no programs that allow you to block thinking in your native language and guarantee quick acquisition of direct thinking skills in another. This goal is not only not implemented, but it is not even put.
2.2 Features of language skills formation in e-AMS
New generations of teachers and students have, as before to wonder how to make the process of mastering the language quality and time-saving, what method to use in teaching. As sad practice shows, mechanical mastery of the language, unfortunately, does not provide any quality or effectiveness of the results, i.e. the ability to complete communication. This result is more natural than accidental: the focus of traditional communication techniques on practical purposes is actually unjustified, since the formal speech approach does not provide real conditions for the formation of psychophysiological mechanisms that ensure the competent use of the language.
2.2.1 Approach to measuring speech skills in a foreign language
The mechanism of speaking any language is set genetically, and it can not be simply changed. As the outstanding Russian scientist academician Shcherba said “We can banish the native language from textbooks and classes, but we cannot banish it from the student’s head” [2]. Figure 2 shows a simplified diagram of the interaction of speakers of different languages within the same subject area. For example, if the work on a joint project in the IT sphere is carried out by specialists from different countries, their linguistic interaction should be considered not in the field of the entire language space, but only in the part that relates to their professional activities. Similarly, it makes no sense to train refugees for a long time in all the intricacies of a new language, it is necessary to give them the skills of a new profession as soon as possible and to ensure full communication within this subject area.
Figure 2.
Interaction between native speakers of different languages within the same subject area.
The main form of communication is the transmission of the necessary meaning through dialogue. The minimum possible fragment of the dialogue (Figure 3) is an elementary Question, the simplest Answer and the Delay between them. In case of a conversation between partners speaking different languages an additional delay Δ is inevitably added to the process of their dialogue related to the internal translation of the information received from the language of the question to its presentation in their minds in their native language. The same process occurs in the opposite direction when forming the answer.
Figure 3.
Fragment of dialogue between native speakers of different languages.
Considering the same processes from the point of view of training or rather the formation of language skills in a foreign language, we can safely say that either the teacher or any training system should strive to compensate as quickly as possible for this internal delay Δ → 0. Then the language zone of the new language will be formed in the consciousness of the learner, or in other words the curve of learning a foreign language will reach the point of “spontaneous speaking”. That is, the construction of the ideal process of learning a foreign language is reduced to the creation of such learning tools guarantee the complete elimination of the delay Δ = 0 in the shortest possible time.
It takes at least 100 hours to master the skill of spontaneous speech. This means that we are not talking about continuous speaking, but about completing 30-minute tasks daily, taking into account interval repetitions twice a day (in the morning and in the evening). Such a schedule should be created by the joint efforts of an adult student and a teacher at least in the first 40 days of the language skills development process. The habit developed during this time will allow the student to reach the level of spontaneous speech, depending on his abilities, after about 3–6 months of continuous training, while making extensive use of the structural-visual method. Implementing this approach will not disrupt the continuity of learning.
2.2.2 Structural-visual method in linguistics
For most people instead of a speech mechanism a translation mechanism works introduced by the school approach to language learning, which is physiologically a different process, the opposite of language. To start the speech mechanism, you need to create a database of sound images, speech-motor images and direct connections of these images with meanings.
The translation mechanism prevents the creation of such links and leads to the formation of links between the signs of one and another language. Therefore, after school experience even in non-translation courses on the communicative method or when using programs like “Rosetta stone” most students choose their usual translation strategy. At the same time, acts of thinking and understanding are performed in the native language, and externally language actions (listening, speaking, and communication) in the target language.
Teachers do not yet have the tools to identify, measure, and change the type of mental process, and to find out what process is actually going on. And it is almost impossible to explain these subtleties to the student in order to obtain self-esteem, especially since these processes are unconscious and do not lend themselves to conscious regulation. The relations between processes in reality, sensory and motor processes in the psyche and language are extremely difficult to explain and poorly understood, and have long been the subject of disputes and disagreements between various sciences and scientific directions. Therefore, consciously explaining to the student what, how and why he needs to do is not a solution to the problem. As a result, the habit of the student, developed earlier in the course of a long experience of studying at school or other institutions, still takes over.
Psycholinguistic studies have shown that the use of grammatical rules for planning and controlling utterances slows down speech activity [3], since the same areas of the brain and mental processes that are needed for understanding or producing speech are involved in operating with the rules.
The Structural-Visual Method, which replaces complex text rules with corresponding visual structures in the form of drawings, diagrams and diagrams, allows us to get out of this contradiction. The application of SVM in linguistics consists in the use of graphical means to demonstrate the structure of an English sentence and how it is constructed in various forms with extensive use of color for encoding meanings.
Replacing verbal Rules with Visual Models (VMs) allows you to make significant improvements to any training method. Instead of hindering language activity, SVM allows you to consciously manage the training of professional skills and very accurately control the process of forming language skills. This removes the acquisition-learning contradiction and turns the grammar monitor into a grammar scaffold. Visual models allow you to quickly launch the speech mechanism and provide not only a comprehensible input, but also a comprehensible output, which in Krashen’s theory rightly seemed inefficient [4].
SVM reveals the mechanisms of practical use of both visualizations of the first kind, which include visual dictionaries and virtual classes, and visualizations of the second kind, which are embodied in Dynamic Grammar, as well as Visual Models based on it [5] and map-tables for different levels of foreign language acquisition.
Leontiev defined visualizations of the second kind as “the external support of internal actions” [3]. Such tools for describing the grammar of a foreign language should be available in every linguistic class, and then only a thoughtful look is enough to understand the structure of the language. The subsequent training of professional skills will allow you to consolidate and transform the consistently formed fundamental grammatical skills into solid skills of speaking a new language in the form of a conscious statement or dialogue. In their significance, Visual Models and Dynamic Grammar are comparable to the periodic table of chemical elements, which hangs on the wall in any classroom where chemistry classes are held.
Figure 4 shows the stages of development of elementary action [6] on the example of the verb Do, where the black arrow indicates the direction of development of the elementary process and the flow of time. The figures below the arrow indicate what is happening in reality, and above the arrow is in the mind. Moreover, the dynamics of the development of an action can be clearly traced from the idea to its completion and is quite accessible to any adult student.
Figure 4.
Dynamics of change of the verb DO.
The extended dynamics (Figure 5) is created by highly qualified linguists and is difficult for students to understand in the first stages of training. In the future, each visual grammatical construction is transformed into a Visual Model of the appropriate level with such assumptions, when the illustrative material is correlated with a specific task of mastering professional material, with specific educational actions. Then the teachers know exactly what the introduction of visibility is necessary for in each particular case, and submit Visual Models in combination with map tables in the form in which they can best perform the corresponding professional task.
Figure 5.
Extended dynamics of change of the verb DO.
Without basic grammatical skills, the same communicative approach turns into simple memorization of spoken phrases. Visual structures of English sentence construction allow for conscious practice, that is, independent planning of the utterance and control of its correctness. It is proposed to focus primarily on mastering the system of English tenses and automation skills, and to postpone the variety of communicative situations for subsequent practice. If you work out the basic construction on a limited number of vocabulary to full automatism, then their use in the future will not cause difficulties and will not require conscious control by the rules. Language acquisition is much faster when there are ready-made algorithms with which the language “works” than when you try to independently derive these algorithms from the speech stream and communicative situations.
A simple video lesson using Visual Models of the English Language for beginners can be viewed at the link Video 1. More detailed information about SVM is provided in [7].
In combination with Visual Models [8, 9], lingvomaps are used (Figure 6) and card tables that provide a mode of accelerated formation of grammatical skills. According to the Krashen’s input hypothesis of the understandable input material, which is in good agreement with Vygotsky’s theory of the Zone of proximal development (the methodological principle of potency) [10], the development of skills occurs when performing exercises that are one step more complex than the current learned level. Increasing the current level can be achieved by increasing the speed and accuracy of the material of the same level of complexity (advanced training), or the introduction of new material (new knowledge-competence-skills).
Figure 6.
Lingvomap.
Since the learning curves (acquisition of knowledge) and learning skills (acquisition of skills) have significantly different forms and numerical parameters, unjustified transfer of methodological techniques from one field of application to another leads to unreasonably slow progress or its complete absence. The choice of the optimal mode of teaching skills in the field of grammatical skills is complicated by the lack of a mathematical description of the regularities of this process, almost complete lack of accurate data and available means of obtaining them. This is due to the extreme complexity and multilevel structure of the language, which is a fractal system with a huge number of interrelated variables, mathematically not strict and ambiguous.
Dynamic Grammar can be used as an independent tool that allows you to bring “correct speaking” into everyday speech activity, but its most effective seems to be to use it as part of interactive speech simulators as part of an electronic acquiring management system (e-AMS).
2.2.3 Language competence management
Forgetting information is fast enough, but electronic Acquiring Management System will ensure the formation of a logarithmic dependence of the learning curve, will avoid the “barrier to overcome” and prevent the transition of training in the mode of retraining (Figure 7).
Figure 7.
Comparison of guided and unguided learning processes.
The proposed e-AMS is primarily aimed at developing listening and speaking skills, which are fundamental [11, 12, 13] and account for about 75% of the total required competencies (Figure 8). As we can see, written speech accounts for less than 10% of the total language competence, so it is not taken into account in our system. In addition, there are several variants of perfectly functioning Internet systems that are specifically designed for learning grammar in writing and can easily be added to e-AMS.
Figure 8.
Distribution of language competences.
In fact, the description of the process of obtaining the necessary result i.e. achieving the level of “spontaneous speaking” is reduced to a mathematical dependence:
Rez=FminmaxLi,Sp,Re,Wr≈350hours,
where: Li – listening, Sp – speaking, Re – reading, Wr – writing.
Moreover, particular importance attaches to the correlation between RLi+Sp, RLi+Re and RLi+Wr, because the process of forming language skills is not determined by simple listening, but works only if the learner strives to imitate the sounds of new speech as accurately as possible and understands the structure of the language at an accessible level.
Therefore, regular lessons (Figure 9) in e-AMS of 25–30 minutes each should consist of:
pre-recorded introductory part (3–5 minutes) in which the teacher explains the meaning of the lesson, and only in this part of the lesson explanations in the student’s native language are allowed, if the visualization mechanisms are not enough to convey the meaning;
main part (about 20 minutes) can be spent in a virtual classroom for the chosen profession, this part of the lesson uses the main components of SVM, which are Visual Models and Visual Dictionary;
final part (5–7 minutes) is a grammatical training based on maps-tables, fixing the language skills at the appropriate level.
Figure 9.
Schematic drawing of a typical lesson structure in e-AMS.
Successful conscious training requires two modes: linear demonstration of patterns to implement them, and random training to fully automate the skill. The first mode shows the linear change of one parameter with fixed other variables. When using the second mode, you can combine linear and random changes to these parameters to create unpredictable situations that require real thinking using language to complete the task.
A video example that briefly explains the meaning of such lessons is provided at the link Video 2.
Currently, the first prototypes of interactive trainers using visual models are created. To implement the full functioning of the e-AMS, 3–7 standard simulators are required at each of the increasing levels of language proficiency in accordance with the program of formation of professional skills of the student. The total number of trainers is about 25.
It should be well understood that the main contradiction that prevents the creation of effective tools for rapid mastery of another language is the logical closure between the language as a tool for managing activities and the language as a subject of activity. The essence of this problem is that if the student does not know how to make a proposal that conveys the desired meaning, then he will not be able to do it. But if he knows the rule of how to do it, then he still can not do it, since the area of the brain responsible for speaking is busy thinking about this rule.
However, one of the most effective ways to teach a language is to learn through the simultaneous formation of professional skills. The proposed approach allows you to form a speech zone based on the study of not individual words and grammar, but the use of typical situations, working dialogues, short sentences, phrases, speech patterns, and even slang. The general view of the process of forming a new speech zone is shown in Figure 10.
Figure 10.
General view of the process of forming a new speech zone using e-AMS.
Teaching materials should be presented in about 90 video tutorials and about 60 training exercises. The corresponding Common European Framework of Reference (CEFR) [14] test system is quite suitable as a control test for the transition from one level to another with one reservation the system must be adapted to the planned professional activity of the student and implemented in two stages at each level (Figure 11).
Figure 11.
Approximate order of using Visual Models according to the learning curve.
Thus, the development of a new language is a transition to a new way of thinking, causing speech production in accordance with the grammatical schemes and semantic connections of the system of the studied language It is here that the gnoseological roots of SVM are found, which is used in the proposed e-AMS.
3. Electronic system for acquiring language skills
The conceptual solution is based on the use of mechanisms of system analysis and unification of scientific concepts of representatives of the Soviet school and scientists of Western countries against the background of the technological leap of the second decade of the 21st century. E-AMS allows you to manage the process of forming professional and language skills of adults and combines:
methodological principles justified in the works of Bandura and Galperin [15];
Structural-Visual Method that accelerates the achievement of linguistic competencies both at the initial stage of learning a foreign language and at the stage of “barrier to overcome”;
using the achievements of the IT sector as a tool for ensuring the implementation of training goals with continuous monitoring of the current state and obtaining a guaranteed learning result in a finite number of steps.
3.1 Structure e-AMS
The prototype being created is a distributed system for managing the acquisition of professional and language skills by adults. The main components of the system [16] (Figure 12) are: Interactive Speech Trainers with Visual Models (IST with VM), a Content Management System (CMS), a Speech Recognition and Modeling System (SRS), a System for Continuous Evaluation (CES) of the level of language skills and a Virtual Assistant (VA).
Figure 12.
Generalized structure of e-AMS.
This training system has the properties:
unity of the main goal of learning outcomes for all its elements;
high stability of the entire system and the independent value of each element;
correlation between the elements of the system, providing positive feedback in the process of formation of professional and language skills;
continuous evaluation of the level of competence of the student, which ensures the formation of a logarithmic dependence of the learning curve and compensates for the prerequisites for the degradation of the learning curve in the direction of loss of expected competence;
ability to evaluate and compare the results of similar educational systems and technologies, as well as their individual components;
cross-platform in relation to operating systems and programming languages, and invariance in relation to the student’s native language and the foreign language being studied.
Interactive Speech Trainers are a new generation of chat bots that use artificial intelligence mechanisms to configure each student in the system, and Visual Models are in the form of augmented reality elements. This combination will allow you to fully immerse yourself in the process of forming skills and guarantees the maximum concentration of the student’s attention.
Content Management System is designed to ensure the formation of educational material based on the most frequent words and semantic structures from the Corpus of the English language, subsequently modified by the subject area of the acquired professional skill. The current frequency is automatically determined using the Google Ngram Viewer resource.
In the course of work of the Continuous Evaluation System, a detailed statistical analysis of the results is carried out, the dynamic learning curves of each adult student are derived, the coefficient tables are justified and the indicators of the speed of formation of speech skills are clarified, the levels of speaking from initial to spontaneous are evaluated in accordance with the CEFR scale [14]. Not the curriculum, and the level of professional skills and language proficiency in the current time will be the determining to move forward on the learning curve.
Special methods of using a Virtual Assistant will allow you to organize the process of training language skills in such a way that the inclusion of the translation mechanism could not happen physiologically. The system will feed the material at such a speed that the exercise can only be performed without translation. Another option is also possible, which is used now, when the target speed of simple exercises is set such that it is almost impossible to perform them in a transferable way in the allotted time.
In addition, the VA must perform the function of a media aggregator, which provides the ability to connect to various services and find interesting professional content (virtual classes, photos, audio, and video) according to various criteria with reference to the level of current language proficiency of an adult student.
E-AMS continuously operates at three levels:
moderator-programmer who is one of the development team;
teacher who updates content, processes general learning statistics at different levels, and in complex cases decides on the teacher’s work through interactive platforms “face-to-face” with the student;
the most important level is the level of an adult student who consciously “transfers” his own consciousness to the control circuit of the educational process.
Of interest is the Visual Representation of e-AMS (Figure 13), which is an attempt to display all of the above in one figure. In fact, this is a visual representation of the theory of interiorization, or the creation of an indicative basis for mental activity according to Halperin, implemented in the adult learning process using e-AMS.
Figure 13.
Visual Representation of e-AMS.
3.2 System architecture
Currently, the development of the main components of e-AMS is carried out in four interrelated areas:
further improvement of the visual approach that models the structure of the mastered activity based on an interactive visual dictionary and visual models;
creation of a set of interactive speech simulators with elements of augmented reality, video lessons and training exercises corresponding to different levels of students’ competence;
development of a content management system, formation of repositories of educational materials for video tutorials and mechanisms for managing the synthesis of educational exercises;
setting up a system of continuous evaluation and management of the learning process in real time.
The last two directions of development of e-AMS are connected exclusively with Big Data. The language system is not limitless and in fact is quite observable, but its full use presents numerous opportunities for combining, which until recently exceeded all possible system conditions, and only now the use of Big Data technology can successfully solve these problems.
In addition, a system of continuous evaluation and learning management provides the configuration of the entire system at the levels of:
interface of speech simulators, where all the achievements in the field of gamification, socialization and cooperation should be fully provided for the translation of the educational process into a modern, intensive and effective format;
continuous measurement and control of the learning process of each individual point of a learning curve or retraining in real time, which provides an individual approach to individual skill training, as the rate of exercise is not defined by an external statement or interface training program and abilities and competence level of each individual student.
Figure 14 shows the system architecture for the first three main parts of the e-AMS: IST with VM, CMS, and SRS [17].
Figure 14.
System Architecture.
Implementation of the platform. The Platform will be implemented using the C# and JavaScript APIs. Since the advent of the HTML5 standard, there have been new opportunities to develop robust and efficient APIs that support voice processing, and this powerful, widely accepted standard includes interaction with various media, protocols, and programming languages [18]. The HTML5 page can handle voice and speech recognition recorded directly from devices, possibly available on the user’s hardware. In addition, WebGL programs consist of control code written in JavaScript and special effects code (shader code) rendering in HTML, use the development element to draw WebGL graphics for visual programming with the introduction of programmable visual effects (for example, elements of augmented reality in the form of VM) and interact with the web page using scripts.
The web server contains a client-side API for transmitting the received data from the database (DB) server to the graphical interface for the client and connecting from the web server to the database using data access layers. These layers contain a connection manager and business layers containing a set of objects (classes) that return data as a set of data for use by web services and JavaScript and WebGel using Google services for speech modeling and speech recognition.
Database design. These are relational data tables that contain information about the lessons, as well as all information about the results of the lessons for each student for further extraction into the system. The results about any student and the lessons conducted with them will be stored in the DB, and dynamic learning curves will be formed on their basis.
Information Request and Display Module (IRDM). In this module, images representing action verbs in sentences are displayed as action and VM images. This module allows the teacher to control the program parameters via the keyboard and computer screen, and this information is sent to the web server: once the information is processed the selected data is displayed to the teacher as the content of the next lesson.
Knowledge Data Formation Module (KDFM). The module consists of a DB that contains all the relational tables and is used to describe and represent the area of knowledge defined by the topic of the classes. The DB contains stored procedures that provide a way to create lesson content at the teacher’s request depending on the selected level of complexity.
Speech recognition module (SRM). This module allows the student to interact in our platform using voice. The system returns the information to the SRM for conversion to speech using Text-to-Speech (TTS). The user speaks into the microphone, and this information is converted into text in the Automatic Speech Recognition (ASR) system. In the web server the java script functions check the correctness of the displayed text in the IRDM from this input data. Finally, the feedback of speaking will be permanently stored in the DB to evaluate and improve the transmitted meaning and pronunciation at the same time.
The introduction of modern ICTs into the structure of the foreign language LMS, along with the use of effective learning models, allows to speed up this process and increases its success by transferring the synergistic effect to all stages of the formation of language skills, especially to the “barriers to overcome” to accelerate the return to the expected competence curve (Figure 15). This approach opens up a new scientific direction in the construction of LMS.
Figure 15.
The learning curve with the transfer of synergistic effect.
4. Conclusions
E-AMS is a unique science-intensive high-tech product. Its use will lead to a synergistic effect in the learning process and, as a result, the accelerated creation of a new language zone in the minds of adult learners. The system provides a process of controlled formation of professional and speech skills, which allows you to move from language learning to its improvement in the process of use. It is created on a modular basis, so each element of the system can be effectively developed and used separately. At the same time, the effect of sharing all elements of the system and integrating additional developments can significantly exceed the capabilities of existing analogues and help solve very important social problems.
In the European Union as part of the Horizon 2020 program, a preliminary assessment of the material and time resources for the creation of a border system combined with the necessary infrastructure for the implementation of the task of training professional personnel and language adaptation of refugees and migrants was carried out. Such expenses amount to approximately 5 million euros, which should be allocated over 2 years, provided that the necessary work is carried out in parallel between 5–6 institutions or organizations.
Within the framework of the current Horizon Europe program, the amount of resources allocated will certainly be increased since apparently the EU leadership does not have a constructive understanding of how to turn the existing migration crisis from a state of humanitarian disaster into a process of managed migration. The consistent appearance of even individual components of e-AMS will contribute to improving the quality of the educational process in the field of foreign language acquisition and expanding international relations.
The work is in its initial stage and researchers from Ukraine, Lebanon, the United States, Japan, and Belarus are involved in the project to some extent. It should be emphasized that it does not contradict the existing system of assessment of language competencies in Europe, but rather contributes to increasing their importance. The role and place of e-AMS in the Cambridge exam system and the assessment systems they have developed is shown on the right side of Figure 16.
Figure 16.
CEFR and the new approach.
I would like to note that the documents prepared by the Council of Europe on the basis of the recommendations of Cambridge and other educational institutions of the United Europe, talk in detail about multi-stage levels of language proficiency, but they do not say anything about how to achieve this in the minds of adult students. It seems that these organizations have a poor understanding of how to do this in relation to adult learners in a fairly short time and on a mass scale.
That is why the problem of refugees and migrants sometimes becomes a disaster in the developed countries of Europe and the United States of America. One of the main reasons why developed countries refuse to accept them is, first of all, a lack of understanding of how to solve the problem of training a large number of adults with their subsequent employment. Therefore, both in Europe and in the United States, instead of destroying walls like the Berlin Wall, they plan and create walls between states, spending huge sums on this, exceeding the cost of creating high-level educational systems by thousands of times (Figure 17). Maybe now with the arrival of the new White House administration, the situation will change?
Figure 17.
The Wall and e-AMS costs.
The implementation of such a project is particularly important for refugees and migrants. In a relatively short time, they will be able to get not only all the necessary information about their new profession, but also to acquire stable language skills of the new homeland. Moreover, the learning process will take place remotely, without the work of a teacher directly in the classroom face-to-face with students, and the classes themselves can be held at any point where there is an Internet connection. The resulting solutions and tools will actively contribute to the efforts of state administrations to manage the integration of migrants at the national and local levels. They will also facilitate communication with migrants and their access to services such as vocational and language training, employment, education and social security in host communities.
Expanding the influence of SVM in educational institutions in different countries and especially the widespread introduction of components in the market of electronic educational services will change the trends that have existed for decades and eliminate existing gaps in the ratings of even developed world powers. For example, such a technologically advanced country as Japan, according to the EF EPI Rating [19], is only on the 55th place in the world, which is significantly lower not only, say, Italy (30) or Spain (34), but even Belarus (40). Despite the existence of the project IT-country (Figure 18), the implementation of a full e-AMS in Belarus is not planned in view of the utter disinterest of the educational structures to change the current situation for decades, are encouraged to seek independent decisions within the system startups or search for like-minded people abroad.
Figure 18.
IT Country Project.
Progress is impossible to stop, however sometimes it is the education system because of the extreme conservatism and focus of their activities primarily on the implementation of government social orders do not contribute to the innovative development of society. An alternative to this is electronic learning systems can quickly implement and put into practice the most advanced technological innovations.
The proposed e-AMS provides the formation of the speech zone of the acquired language in the mind of the learner due to a sharp increase in the effectiveness of exercises by transforming grammatical information from verbal to graphic form. In addition, continuous assessment of the level of competence of the student leads to the formation of a logarithmic dependence of the learning curve and compensates for the prerequisites for its degradation, providing a synergistic effect in the learning process. As the core of the system, it is planned to use the Structural-Visual Method to create non-Verbal means of teaching foreign languages based on modern technologies, teaching new labor skills and new social interaction.
The main result to date can be considered a conceptual solution to the problem of simultaneous interaction of the language system and ICT tools that ensure the sustainable formation of foreign language thinking of adults in the process of developing professional and language skills presented at the system level.
Acknowledgments
I would like to express my gratitude to Anatoly Osipov, Vice-Rector for Scientific Work of the Belarusian State University of Informatics and Radioelectronics (Belarus), for financial support in publishing this chapter.
I would like to express my special gratitude to my co-authors: inventor of visual models Vitaly Dibrova (Studio of Languages, Ukraine) for the provided video materials; Professor Tomokazu Nakayama (Jissen Women’s University, Tokyo, Japan) and Professor Boyd Bradbury (Minnesota State University Moorhead, USA) for their sincere support of the idea of creating e-AMS; Professor Igor Schagaev (IT-ACS Ltd., London, UK), whose scientific ideas inspired me to develop e-AMS and write this chapter.
I also thank my graduate students: Imad Tahini (Lebanon) for creating a working prototype that demonstrates the interaction of the three main parts of e-AMS and Artsyom Marzalyuk (Belarus) for creating the drawings in this chapter.
\n',keywords:"continuous evaluation, learning curve, learning management system, synergistic effect, visual approach",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76448.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76448.xml",downloadPdfUrl:"/chapter/pdf-download/76448",previewPdfUrl:"/chapter/pdf-preview/76448",totalDownloads:332,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 23rd 2020",dateReviewed:"March 1st 2021",datePrePublished:"April 24th 2021",datePublished:"November 17th 2021",dateFinished:"April 24th 2021",readingETA:"0",abstract:"The relevance of the research topic is determined by the need to create a new technology for applying the most effective methods of forming speech skills of foreign language proficiency in adults. The research is interdisciplinary and is located at the intersection of psychology, pedagogy, linguistics, information technology and systems analysis. The goal is to implement a visual approach in an electronic educational environment to accelerate the acquisition of a new language by adults. The peculiarity of the approach is the logical relationship of the entire language system, time-saving learning of the material and the dynamics of the use of language structures. The proposed training management system is a distributed system for managing the formation (acquisition) language skills of adults. In the course of the system’s operation, a detailed statistical analysis of the results is carried out, dynamic learning curves of each student are displayed, coefficient tables are justified and indicators of the formation of speech comprehension skills by ear are specified, and speaking levels are determined from the initial to the threshold level of spontaneous speaking. The conceptual solution of such a system has been obtained, and its use will lead to a synergistic effect in the learning process and, as a result, the accelerated creation of a new language zone in the student’s mind. The system provides a process of controlled formation of speech skills to a threshold level that allows you to move from learning a language to improving it in the process of using it. Continuous evaluation of the level of competence of the learner leads to the formation of logarithmic learning curve and compensates for the prerequisites of the degradation of a learning curve in the direction of loss of expected competence, which opens up a new area of search in building learning management systems.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76448",risUrl:"/chapter/ris/76448",signatures:"Aliaksei Dadykin",book:{id:"10260",type:"book",title:"Digital Service Platforms",subtitle:null,fullTitle:"Digital Service Platforms",slug:"digital-service-platforms",publishedDate:"November 17th 2021",bookSignature:"Kyeong Kang",coverURL:"https://cdn.intechopen.com/books/images_new/10260.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-102-7",printIsbn:"978-1-83969-101-0",pdfIsbn:"978-1-83969-103-4",isAvailableForWebshopOrdering:!0,editors:[{id:"2114",title:"Dr.",name:"Kyeong",middleName:null,surname:"Kang",slug:"kyeong-kang",fullName:"Kyeong Kang"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"341113",title:"Associate Prof.",name:"Aliaksei",middleName:"Kuzmich",surname:"Dadykin",fullName:"Aliaksei Dadykin",slug:"aliaksei-dadykin",email:"alex_05_07@rambler.ru",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Prerequisites for creating a system for acquiring language skills",level:"1"},{id:"sec_2_2",title:"2.1 Typical systems for teaching a foreign language",level:"2"},{id:"sec_3_2",title:"2.2 Features of language skills formation in e-AMS",level:"2"},{id:"sec_3_3",title:"2.2.1 Approach to measuring speech skills in a foreign language",level:"3"},{id:"sec_4_3",title:"2.2.2 Structural-visual method in linguistics",level:"3"},{id:"sec_5_3",title:"2.2.3 Language competence management",level:"3"},{id:"sec_8",title:"3. Electronic system for acquiring language skills",level:"1"},{id:"sec_8_2",title:"3.1 Structure e-AMS",level:"2"},{id:"sec_9_2",title:"3.2 System architecture",level:"2"},{id:"sec_11",title:"4. Conclusions",level:"1"},{id:"sec_12",title:"Acknowledgments",level:"1"},{id:"sec_12",title:"Video materials",level:"1"}],chapterReferences:[{id:"B1",body:'Bandura A and Walters R. Social learning and personality development. New York: Holt, Rinehart, & Winston; 1963'},{id:"B2",body:'Scherba L. Language system and speech activity. Leningrad; 1974'},{id:"B3",body:'Leontiev A. Language and speech activity in General and pedagogical psychology. Moscow Psychological and Social Institute, Moscow; 2004'},{id:"B4",body:'Krashen S. Principles and Practice in Second Language Acquisition. University of Southern California; 1982. 202 p'},{id:"B5",body:'Tahini I, Dadykin А and Dibrova V. The Model of Change as the Basis of the Knowledge Structure in the Next Generation E-LMS. In: Proceedings of the 10th annual International Conference of Education, Research and Innovation (ICERI 2017); 16-18 November 2017; Seville, Spain. p. 5022-5032. DOI: 10.21125/iceri.2017'},{id:"B6",body:'Tahini I and Dadykin А. Techniques of new application for acquisition foreign languages: development stages and modern trend. In: Proceedings of the 9th International Conference on Digital Information and Communication Technology and its Applications (DICTAP 2019); 3-5 April 2019; Beirut, Lebanon. France: EDP Sciences – Web of Conferences, Section E-Learning 27; 2019. 7 p. DOI: 10.1051/itmconf/20192706001'},{id:"B7",body:'Dadykin A. Information and Communication Technology and Callan Method. In: Proceedings of the 2020 International Conference on Education Development and Studies (ICEDS 2020); 3-5 March 2020; Paris, France. New York: Association for Computing Machinery. p. 44-52. DOI: 10.1145/3392305.3396905'},{id:"B8",body:'Dadykin А, Dibrova V and Tahini I. The Visual Approach in Educational Projects. International Journal of Social Science and Humanity. 7, 6; 2017. p. 373-377. DOI: 10.18178/IJSSH.2017.V7.851'},{id:"B9",body:'Tahini I, Nakayama Т, Dibrova V and Dadykin А. Cognitive Psychology Models and Approaches to Develop Language Skills. International Journal of Social Science and Humanity. 8, 4; 2018. p. 110-115. DOI: 10.18178/ijssh.2018.V8.945'},{id:"B10",body:'Vygotsky L. Thought and language. Cambridge, MA: MIT Press; 1934/1986'},{id:"B11",body:'Wilt, Miriam E. A study of teacher awareness of listening as a factor in elementary education. Journal of Educational Research, 43, 8; 1950, p. 626-636'},{id:"B12",body:'British Council [Internet]. Five essential listening skills for English learners By Raphael Ahmed. 2015. Available from: https://www.britishcouncil.org/voices-magazine/five-essential-listening-skills-english-learners. [Accessed: 2018-11-30]'},{id:"B13",body:'University of Missouri Extension [Internet]. Listening: Our Most Used Communications Skill. Available from: https://extension2.missouri.edu/cm150. [Accessed: 2018-11-30]'},{id:"B14",body:'Council of Europe Language Policy Portal. 2018 Common European Framework of Reference for Languages: Learning, teaching, assessment (CEFR). Available from: https://rm.coe.int/cefr-companion-volume-with-new-descriptors-2018/1680787989'},{id:"B15",body:'Galperin P. Psychology of thinking and teaching about the gradual formation of mental actions. Research in the thinking of Soviet psychology. Moscow; 1966'},{id:"B16",body:'Bradbury B, Dadykin А and Tahini I. Fundamentals of New Effective System to Accelerate Language Acquisition Using Visual Approach. International Journal of Information and Education Technology. 8, 11; 2018. p. 768-772. DOI: 10.18178/ijiet.2018.8.11.1137'},{id:"B17",body:'Tahini I and Dadykin А. Proposed System of New Generation LMS Using Visual Models to Accelerate Language Acquisition. Advances in Science, Technology and Engineering Systems Journal. 3, 5; 2018. p. 277-287. DOI: 10.17781/P002439'},{id:"B18",body:'Fulton S and Fulton J. HTML5 Canvas. O’Reilly Media Incorporated; 2013'},{id:"B19",body:'Education First English. Proficiency Index. EF EPI. The world\'s largest ranking of countries and regions by English skills, 2020. Available from: https://www.ef.com/wwen/epi/'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Aliaksei Dadykin",address:"alex_05_07@rambler.ru",affiliation:'
Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus
'}],corrections:null},book:{id:"10260",type:"book",title:"Digital Service Platforms",subtitle:null,fullTitle:"Digital Service Platforms",slug:"digital-service-platforms",publishedDate:"November 17th 2021",bookSignature:"Kyeong Kang",coverURL:"https://cdn.intechopen.com/books/images_new/10260.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-102-7",printIsbn:"978-1-83969-101-0",pdfIsbn:"978-1-83969-103-4",isAvailableForWebshopOrdering:!0,editors:[{id:"2114",title:"Dr.",name:"Kyeong",middleName:null,surname:"Kang",slug:"kyeong-kang",fullName:"Kyeong Kang"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"313818",title:"Ph.D.",name:"William",middleName:null,surname:"Elwood",email:"william.elwood@nih.gov",fullName:"William Elwood",slug:"william-elwood",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:null},booksEdited:[],chaptersAuthored:[{id:"71842",title:"Psychological Health Influences of Legal-Marriage and -Partnerships on Same-Sex Couples",slug:"psychological-health-influences-of-legal-marriage-and-partnerships-on-same-sex-couples",abstract:"This chapter explores whether Californians in same-sex legal marriages and partnerships reported lower levels of psychological distress than other adult Californians after the 2008 California Supreme Court Decision that legalized same-sex marriage. We pooled 10 years of California Health Interview Survey (CHIS) data and employ a T1-T2 design to approximate a time series design. Dependent variables include overall self-related health, psychological distress, and household income. Independent variables include sexual identity and same-sex spouse. Bi-variate analyses compared self-reported mental and physical health between the two periods. We found decreased reports of poorer health and increased reports of very good health among gay men and lesbian women with legal spouses. Psychological distress decreased for legally coupled gay men and lesbians while increased slightly among unpartnered lesbian women and gay men. Household income increased among coupled lesbian women and gay men and decreased among others. Our project demonstrated positive health influences for Californians with legal same-sex spouses. We recommend future research projects that explore whether and how same- and opposite-sex marriage benefits health, well-being, and prosperity, and for marital status survey questions that are inclusive of sexual and gender identities and elicit the sex/gender of a respondent’s spouse.",signatures:"William N. Elwood, Veronica L. Irvin, Benmei Liu, Richard Lee and Nancy Breen",authors:[{id:"313818",title:"Ph.D.",name:"William",surname:"Elwood",fullName:"William Elwood",slug:"william-elwood",email:"william.elwood@nih.gov"}],book:{id:"6934",title:"Psycho-Social Aspects of Human Sexuality and Ethics",slug:"psycho-social-aspects-of-human-sexuality-and-ethics",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"149375",title:"Prof.",name:"Donata",surname:"Francescato",slug:"donata-francescato",fullName:"Donata Francescato",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"261445",title:"Prof.",name:"Minou Ella",surname:"Mebane",slug:"minou-ella-mebane",fullName:"Minou Ella Mebane",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"297306",title:"M.D.",name:"Kari",surname:"Schneider",slug:"kari-schneider",fullName:"Kari Schneider",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Minnesota",institutionURL:null,country:{name:"United States of America"}}},{id:"300171",title:"Dr.",name:"Rachel",surname:"Cafferty",slug:"rachel-cafferty",fullName:"Rachel Cafferty",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"300172",title:"Dr.",name:"Joanna",surname:"Ekstrom",slug:"joanna-ekstrom",fullName:"Joanna Ekstrom",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"314134",title:"Ph.D.",name:"Craig",surname:"Nagoshi",slug:"craig-nagoshi",fullName:"Craig Nagoshi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"314135",title:"Ms.",name:"Catherine",surname:"Baggett",slug:"catherine-baggett",fullName:"Catherine Baggett",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"The University of Texas at Arlington",institutionURL:null,country:{name:"United States of America"}}},{id:"314136",title:"Dr.",name:"Julie",surname:"Nagoshi",slug:"julie-nagoshi",fullName:"Julie Nagoshi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Arizona State University",institutionURL:null,country:{name:"United States of America"}}},{id:"316697",title:"Dr.",name:"Oludayo",surname:"Tade",slug:"oludayo-tade",fullName:"Oludayo Tade",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"318220",title:"Dr.",name:"Collins",surname:"Udechukwu",slug:"collins-udechukwu",fullName:"Collins Udechukwu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/318220/images/11605_n.jpg",biography:"My name is Collins Obinna Udechukwu, I have a bachelor’s degree in sociology and a master’s degree in criminology and Penology both from the University of Ibadan Nigeria. I am vast and verse in both quantitative and qualitative research mechanisms, making me have a full understanding of human behaviour, interaction, needs and expectations. Throughout my academic sojourn, I had the privilege of publishing academic journals in the National Association of Sociology and Anthropology and a paper presentation in Toronto Canada.",institutionString:null,institution:{name:"University of Ibadan",institutionURL:null,country:{name:"Nigeria"}}}]},generic:{page:{slug:"orders-and-delivery",title:"Order and Delivery Info",intro:'
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LSR Libros Servicios y Representaciones S.A. de C.V
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\r\n\tIn order to scientifically address significant issues such as climate change, which puts into question our very survival as a species, the current pandemic with its massive physical, socio-economical, and psychological consequences, and the rise of AI which challenges our established economic structures, we need to ask insightful questions: What is truly human? How can humans develop further? The answers to these questions are necessary not only to find new solutions to the current challenges, but also to shape new visions of what can come next.
\r\n
\r\n\tNeuroscientific research linking brain functions has produced a perspective on human development that includes normal, impaired, and enhanced neurophysiological, emotional and cognitive functioning. Human development has been considered the very aim of education and of educative processes. Indeed, the capabilities built through educational training are included in the UN’s human development index, according to which such capabilities are the ultimate criteria to assess the development of a country, rather than economic growth alone. Yet a full understanding of what Human Development truly constitutes, remains open. For example, tackling the question of what distinguishes human beings from other animals, and what humans’ possible development trajectory might look like, calls for a multidisciplinary approach. Consequently, contributions to such an inquiry might come from very different scientific fields, ranging from cognitive neuroscience to socioeconomics. For instance, in the field of neuroscience, self-awareness—the most specific characteristic of human beings—has been investigated in connection with its neural correlates. Recent research points to self-awareness as the particular ability of our species, directly connecting it to our abstract thinking which in turn enables envisioning new possible futures and self-development
\r\n
\r\n\tTo achieve a broad, multidisciplinary perspective on possible human development, subjects will be considered through varied— yet related—approaches. We will provide a complex yet consistent framework through which we will explore a substantial amount and variety of theories and case studies. Our ultimate goal will be to produce useful indications for policy making in diverse contexts, assist teachers and parents with child development in an optimal way, and enhance theoretical and practical knowledge.
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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/313818",hash:"",query:{},params:{id:"313818"},fullPath:"/profiles/313818",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var t;(t=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(t)}()