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Terrain Traversability for Robot Control",slug:"watch-your-step-terrain-traversability-for-robot-control",signatures:"Mauro Bellone",authors:[{id:"158265",title:"Dr.",name:"Mauro",middleName:null,surname:"Bellone",fullName:"Mauro Bellone",slug:"mauro-bellone"}]}]}],publishedBooks:[{type:"book",id:"3711",title:"Robot Localization and Map Building",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"robot-localization-and-map-building",bookSignature:"Hanafiah Yussof",coverURL:"https://cdn.intechopen.com/books/images_new/3711.jpg",editedByType:"Edited by",editors:[{id:"32065",title:"Dr.",name:"Hanafiah",surname:"Yussof",slug:"hanafiah-yussof",fullName:"Hanafiah Yussof"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5263",title:"Robot Control",subtitle:null,isOpenForSubmission:!1,hash:"b6ee44b867a4e27c8e3bae6016c4f698",slug:"robot-control",bookSignature:"Efren Gorrostieta Hurtado",coverURL:"https://cdn.intechopen.com/books/images_new/5263.jpg",editedByType:"Edited by",editors:[{id:"38850",title:"Dr.",name:"Efren",surname:"Gorrostieta Hurtado",slug:"efren-gorrostieta-hurtado",fullName:"Efren Gorrostieta Hurtado"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[]},onlineFirst:{chapter:{type:"chapter",id:"70396",title:"Predictive Direct Torque Control Strategy for Doubly Fed Induction Machine for Torque and Flux Ripple Minimization",doi:"10.5772/intechopen.89979",slug:"predictive-direct-torque-control-strategy-for-doubly-fed-induction-machine-for-torque-and-flux-rippl",body:'\nIn the field of renewable energy sources, wind energy is gaining much importance. The increase in the level of generation has two main restrictions, one is due to the limitations of the switching frequency of the power devices with respect to the power drive of the Doubly Fed Induction Machine (DFIM) and second one is the requirement of good dynamic torque performance, these restrictions are addressed by the proposed new Direct Torque Control (DTC) approach.
\nThe foremost torque control methods proposed were classified into Field Oriented Control (FOC) techniques, Blaschke [1] and the direct control techniques. Then after, the concept of DTC, Takahashi and Ohmori [2] and Direct Self Control (DSC), Depenbrock [3] were introduced to achieve good steady-state and transient torque control conditions. Moreover, direct control techniques do not require current regulators, nor coordinate transformations or specific modulations like Pulse Width Modulation (PWM) or Space Vector Modulation (SVM) for pulse generation. The disadvantages are the lack of direct current control, torque control difficulties at very low speeds and especially variable switching frequency behavior.
\nThe last important drawback put forward several authors in recent years, thus, methods like Direct Mean Torque Control (DMTC), [4] and Direct Torque Control based on Discrete Space Vector Modulation (DTC–DSVM), [5], have already achieved constant switching frequency. Furthermore, the methods proposed in Kang and Sul [6, 7] have extended the solution to reduce torque ripple at the same time imposing the switching frequency, as well as for different voltage-source multilevel topologies.
\nFurther the concept of DTC have been applied to the brushless doubly fed induction machine, [8], or the equivalent Direct Power Control (DPC) strategy for several grid connected converter applications, [9].
\nIn this paper, the analysis on the Doubly Fed Induction Machine (DFIM), which is a common solution for variable speed wind turbines, is discussed. The control methods like FOC have been performed by many authors, for example [10]. DTC and DPC methods without switching frequency imposition have also been carried out in Gomez & Amenedo [11], Datta & Ranganathan [12], while the DPC at constant switching frequency has also been developed in [13].
\nThese mentioned direct control techniques that achieve constant switching frequency behavior are based on predictive control with a prediction horizon equal to one sample period. In this paper, predictive DTC technique for the DFIM will be employed based on [13] and the performance is compared with the DTC strategy, at constant switching frequency and with reduced torque and flux ripples criteria. This control technique is based on a prediction of the torque and the flux evolution of the DFIM. Hence, the new Predictive DTC (PDTC) strategy presented in this paper is based on a direct control of the electromagnetic torque and the rotor flux of the machine. Simulation results are presented and discussed, and at last, the results show that the predictive DTC technique presents good dynamic response compared to classical DTC concept.
\nIn this Chapter, predictive DTC technique for the DFIM has been implemented and its performance is compared with the classical DTC of DFIM based on certain parameters like constant switching frequency, torque ripple and flux ripple. This control technique is based on a prediction of the torque and the flux evolution of the DFIM. To validate the proposed control scheme, results are presented. From these results, it is observed that the predictive DTC technique gives good dynamic response compared to classical DTC concept.
\nThe control strategy even reduces the switching losses of the converter and reduces the electromagnetic torque and flux ripples at low switching frequency even under variable speed operating conditions.
\nIn Section 1, the introduction of the Chapter is given.
\nIn Section 2, Contributions and Novelty of this Chapter is explained.
\nIn Section 3, Modeling of the DFIM is given.
\nIn Section 4, the basic control principle of predictive DTC is explained and also implementation of the proposed predictive DTC strategy of DFIM along with selection of rotor voltage vectors for constant switching frequency and reduction of switching power losses are described.
\nIn Section 5, Results are presented to validate the proposed control strategy.
\nIn Section 6, the Conclusions of the Chapter are described.
\nIn Section 7, the summary of the Chapter is given.
\nThe main contributions of this Chapter are as follows:
Implementation of a new predictive Direct Torque Control (DTC) strategy of the Doubly Fed Induction Machine (DFIM) is presented which is designed to operate at a low constant switching frequency.
The proposed DTC method effectively reduces the torque and flux ripples at low switching frequency, even under variable speed operation conditions.
Results are presented to validate the proposed control strategy.
The novelty of this Chapter is that the predictive DTC have been developed to control the different parameters of DFIM, to improve its performance during transient, steady state, tracking behavior and operation near synchronous speed. All these cases are thoroughly investigated.
\nThe DFIG model adopted is the qd0 rotating reference frame. It is because the model of DFIM is quite suitable with this frame of reference during transients. The transient solution of the DFIM model is possible because of the transformation from abc to qd0 by which the differential equations with time-varying inductances is converted into differential equations with constant inductances,
\n(or) simply (1) can be written as
\nSimilarly, the q and d-axis rotor voltages referred to the stator are given by,
\n(or) simply (3) can be written as
\nThe stator and rotor fluxes can be calculated by using (5) and (6) can be used to calculate the magnitudes.
\nThe electromagnetic torque of DFIM is given in (7).
\nThe active and reactive powers are given by:
\nThe main drawback of classical DTC scheme is nonconstant switching behavior; it is avoided by the proposed predictive Direct Torque Control strategy. The constant switching behavior is achieved by increasing slightly the complexity of control strategy.
\nA sequence of three voltage vectors will be introduced at a constant switching period of which two are active vectors always followed by a zero vector, in order to reduce the ripples of both directly controlled variables compared to selection of four rotor voltage vectors depending on the position of rotor flux in classical DTC scheme. For that purpose, ripple reduction criteria based on a prediction of the electromagnetic torque and rotor flux evolution over time is implemented which is derived from (7) and (10) [13] and illustrated in \nFigure 1(a)\n.
\n(a) Simplified prediction scheme of torque and rotor flux of DFIM. (b) Variation of electromagnetic torque with zero vectors at constant torque and rotor flux.
The basic principles of the predictive DTC scheme are firstly, constant switching period ‘ts’ is defined. In predictive DTC, the control procedure is discretized unlike in DTC scheme, which is based on time domain. Secondly, at steady state condition, by taking the electromagnetic torque and flux amplitude errors to be minimized, the three different rotor voltage vectors are injected during ts. Note that according to the chosen three vector sequence together with the specified time intervals for each vector, the electromagnetic torque and flux evolutions within the switching period can be different. Thirdly, this procedure is repeated at constant period ts.
\nThe Doubly Fed Induction Machine is modeled using (2), (4)–(6). The torque is calculated by using (7) in terms of complex conjugate of rotor flux and stator flux.
\nUsing (2), (4)–(7), the predictive expressions for torque and rotor flux are given by [2, 13]
\nThe below space vector representations are used in order to analyze the expressions (9) and (10).
\nwhere
where
\n\nEq. (15) comprises of a cosine term and two constant terms, the cosine term with depends on DC bus voltage and it indicates that the cosine term of rotor flux variation is constant for zero vectors and only depends on active vectors.
\n\nEq. (10) can be simplified considering the stator flux vector module which is nearly constant.
\n\nEq. (17) is similar to rotor flux derivation with only one constant and one sine term.
\nEquations (15) and (17) are used practically as shown in \nFigure 1(a)\n, instead of (9) and (10). As shown in \nFigure 1(a)\n, the torque and flux derivatives depends on only four terms when considering the DC bus voltage, stator voltage and flux as constant magnitudes. In the expression (17), the constant term depends on Tem and, that means it depends on machine operating condition, which can be positive or negative as shown in \nFigure 1(b)\n. Because of this reason only, the phase shift order of each active vector varies, whereas, it is not like that for rotor flux derivative. The key point is knowing the slopes or the derivatives of torque and flux for each of the rotor voltage vector based on \nFigure 1(a)\n and \nTable 1\n and considering these slopes to be constants within the specific time in the given switching period, ts, the torque and flux ripples can be maintained within the limits.
\n\n | Error of electromagnetic torque | \n||
---|---|---|---|
\n | 1 | \n−1 | \n|
Error value of rotor flux | \n1 | \nV(n − 1)\n | \nV(n + 1)\n | \n
−1 | \nV(n − 2)\n | \nV(n + 2)\n | \n
With constant values
\nFrom the \nFigure 1(b)\n, it can be seen that the slope of torque derivative varies proportionally to the slip speed provided at fixed torque and rotor flux operating conditions and also near synchronous speed, the slope of the zero vector becomes smaller obeying the fact that from the expression (17) the zero vector produce small torque variation. This fact implies that near synchronous speed the amplitude of rotor voltage vector is small demanding the condition of zero vectors. As shown in \nFigure 1(b)\n, this transition from positive to negative torque slope is different to the synchronous speed.
\nThe first vector is selected depending on the errors of torque and flux and the sector where the rotor flux lies, the look up table for vector selection is shown in \nTable 1\n. From the \nTable 1\n, it is noticed that the required rotor voltage vector should produce either positive or negative slope variation depending on the output of the torque or flux hysteresis comparators that is either 1 or −1, respectively.
\nThe first vector is actually selected based on the classical DTC; the second vector is selected such that it is always followed by zero vectors in order to reduce the torque and flux ripples based on the predictive DTC strategy.
\nFrom \nFigure 2\n, it can be seen that from the derivative calculations block the required values of torque and flux evolutions depending on the each rotor voltage selected are fed to the ripple reduction criteria block, in which based on the slopes calculations by (15) and (17), the required rotor voltage vectors which are selected are active for the time period of this constant slopes. This constant time period of the constant slopes is fed to the switching table, where in the switching operation of rotor voltages are chosen in such a way that it reduces the switching losses.
\nBlock diagram of proposed predictive direct torque control strategy of DFIM.
From (15) and (17), it can be inferred that the rotor flux is constant for zero vector and it produces opposite sign for torque variation for first two active vectors. These two active vectors along with zero vectors are useful to control torque and rotor flux. By the two active vectors the rotor flux as one vector produce positive slope, the other vectors produce negative slope because of this the flux ripple is not eliminated completely as compared to torque ripple and further it affects the rotor and stator currents. \nTable 2\n shows the selection of second active vector, after the first active vector is selected. It clearly shows that, one vector cannot be selected, as the flux would have a very big or small variation, which leads to poor quality of flux output.
\n\n | Active vectors | \n
---|---|
Below synchronous speed | \nV(n + 1), V(n + 2)\n | \n
Above synchronous speed | \nV(n − 1), V(n − 2)\n | \n
The right choice of zero vectors V0 and V7 implies that there is reduction in switching power loss of the converter. Two different switching sequences exist for each pair of required active vectors, which allows the commutations of the converter to be reduced. \nTable 3\n shows the correct sequence of vectors, which allows only four commutations per switching period ts. The candidate sequences are such that it reduces the switching power losses by transmitting the smallest current values, which is the main task of the proposed control strategy.
\nActive vectors | \nSector | \nSwitching sequences | \nZero vector | \n
---|---|---|---|
V1-V2\n | \n6(subs) | \n100-110-111 | \nV7\n | \n
3(hypers) | \n110-100-000 | \nV0\n | \n|
V2-V3\n | \n1(subs) | \n110-010-000 | \nV0\n | \n
4(hypers) | \n010-110-111 | \nV7\n | \n|
V3-V4\n | \n2(subs) | \n010-011-111 | \nV7\n | \n
5(hypers) | \n011-010-000 | \nV0\n | \n|
V4-V5\n | \n3(subs) | \n011-001-000 | \nV0\n | \n
6(hypers) | \n001-011-111 | \nV7\n | \n|
V5-V6\n | \n4(subs) | \n001-101-111 | \nV7\n | \n
1(hypers) | \n101-001-000 | \nV0\n | \n|
V6-V1\n | \n5(subs) | \n101-100-000 | \nV0\n | \n
2(hypers) | \n100-101-111 | \nV7\n | \n
Sequence of vector selection for reduction of switching power losses under steady-state [13].
subs – sub synchronous speed operation, hypers – super synchronous speed operation.
The predictive DTC control scheme of DFIM has been implemented using MATLAB/Simulink. The specifications of DFIM are given in \nTable 4\n. The conditions of steady state, transient, tracking behavior, and performance near synchronism of DFIM are examined, which are given in three subsections.
\nDoubly fed induction machine | \n|
---|---|
Specifications | \nRatings | \n
Rated power | \n2.6 MW | \n
Rated voltage | \n690 V | \n
Synchronous speed | \n1500 rpm | \n
Frequency | \n50 Hz | \n
Number of pairs of poles | \n2 | \n
Stator to rotor turns ratio | \n0.34 | \n
Base voltage | \n398.4 V | \n
Base current | \n2175 A | \n
Base angular frequency | \n157 rads−1\n | \n
Base power | \n2.6 MW | \n
Stator resistance | \n0.0108 pu | \n
Stator leakage inductance | \n0.102 pu | \n
Magnetizing inductance | \n3.362 pu | \n
Rotor resistance referred to stator | \n0.0121 pu | \n
Rotor leakage inductance referred to stator | \n0.11 pu | \n
Inertia constant(H) | \n0.5 s | \n
Friction coefficient(F) | \n0.05479 pu | \n
DC-link capacitance | \n5e–3F | \n
DC-link voltage | \n1200 V | \n
\n | \n|
Number of blades | \n3 | \n
Rotor diameter | \n70 m | \n
Hub height | \n84.3 m | \n
Turbine total Inertia | \n4.4532 e5Kgm2\n | \n
Stiffness constant (B) | \n2 pu | \n
Mutual damping | \n1.5 pu | \n
Cut-in wind speed | \n3 ms−1\n | \n
Cut-out wind speed | \n25 ms−1\n | \n
Rated wind speed | \n15 ms−1\n | \n
Specifications of DFIM and wind turbine.
The performance of proposed control strategy of DFIM is analyzed for steady state and transient conditions. For the transient conditions, the step change in electromagnetic torque i.e., from −0.4 pu to +0.4 pu at 0.6 s is considered. That means, from generator mode (negative torque) to motoring mode (positive torque), with constant switching frequency of 1 kHz, with speed reference of 1350 rev/min, and DC-link voltage of 1200 V. The DFIM is under steady state operation up to 0.6 s with torque of −0.4 pu and at 0.6 s the DFIM enters into transient state, and again it reaches its steady state value of 0.4 pu.
\nThe response of stator currents in stationary reference frame are shown in \nFigure 3(a)\n. Therefore, there are two waveforms which refer to α, β components of stator currents. From the \nFigure 3(a)\n, it is observed that there are no over currents in stator, which indicates the effectiveness of the proposed control scheme even at sudden variation in torque demand. This is possible because of selection of proper rotor voltage vectors with their respective time intervals.
\n(a) Response of stator currents of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (b) Torque response of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s, (i) proposed strategy (ii) classical DTC. (c) stator active power of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (d) Stator flux response of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (e) Response of rotor flux of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (f) Rotor speed response of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s. (g) Response of rotor currents of DFIM for step change in Tem from −0.4 pu to 0.4 pu at 0.6 s.
\n\nFigure 3(b)\n shows the response of developed torque for the proposed strategy and classical DTC strategy (not expressed in p.u. value), from the figure, it is noticed that the torque response of the DFIM closely followed the torque command and also torque ripple is zero.
\nFrom the \nFigure 3(c)\n, it can be seen that stator active power has good dynamic response when the reference torque is changed suddenly. From the figure, it is observed that the stator active power follows the torque demand to make the DFIM to develop the torque to match its reference value.
\nThe response of the stator flux is shown in \nFigure 3(d)\n, from the figure, it is clearly noticed that the stator flux response remains constant which is not affected by variation in torque command.
\nThe response of the rotor flux is shown in \nFigure 3(e)\n, from the figure, it is observed that the rotor flux response is also not affected by change in reference torque and also the rotor flux response is sinusoidal in nature which is not distorted due to sudden change in torque command.
\n\n\nFigure 3(f)\n shows response of the rotor speed of DFIM. From the \nFigure 3(f)\n, it is observed that there is decrease in rotor speed due to step change in reference torque but decrease in rotor speed is very small.
\nThe response of rotor currents is shown in \nFigure 3(g)\n. From the \nFigure 3(g)\n, it is observed that there are no over currents in rotor, which indicates the effectiveness of the proposed control scheme even at sudden variation in torque demand. This is possible because of selection of proper rotor voltage vectors with their respective time intervals.
\nIn this section, the proposed control scheme of DFIM is investigated for variable speed operation implying wind energy applications, and at the same instant, the torque reference may also vary respectively with speed of DFIM. This kind of behavior of wind energy system is called as tracking behavior. At this condition, the actual values of the DFIM should follow the reference values as closely as possible and this is clearly guaranteed by proposed control strategy which can be seen clearly through the results presented in this section. To explore the tracking behavior of DFIM, sinusoidally varying reference torque with amplitude of 0.4 pu and frequency of 3 Hz is set to the DFIM. By this set reference torque, the DFIM operates in generating and motoring modes. In this mode of operation, the other parameters of DFIM are same as mentioned in Section 5.1.
\nThe predictive DTC strategy has good tracking behavior and it is confirmed that the reduction in torque and flux ripples is achieved as there are absolute absence of over currents and reduced ripples in stator currents as shown in \nFigure 4(a)\n. From the \nFigure 4(a)\n, it is noticed that there is continuous increase and decrease in the amplitude of stator currents for maintaining the consistency due to variable behavior of torque command. The stationary reference frame stator currents are clearly noticed in \nFigure 4(a)\n.
\n(a) Response of stator currents of DFIM with variable torque command. (b) Response of developed torque of DFIM with variable torque command (i) proposed strategy (ii) classical DTC. (c) Response of stator flux of DFIM with variable torque command. (d) Response of rotor flux of DFIM with variable torque command. (e) Response of rotor speed of DFIM with variable torque command. (f) Response of rotor currents of DFIM with variable torque command.
The torque produced by the DFIM follows as closely as the reference torque, which indicates good tracking behavior of the proposed control scheme comparative to classical DTC (not expressed in p.u. value), it can be seen in \nFigure 4(b)\n.
\nStator flux and rotor flux responses of DFIM are shown in \nFigure 4(c)\n and \n(d)\n respectively. From the \nFigure 4(c)\n and \n(d)\n, it is observed that the variation in torque command is not having any influence on the stator and rotor fluxes of DFIM.
\n\n\nFigure 4(e)\n shows the rotor speed response of DFIM. From the \nFigure 4(e)\n, it is observed that there is continuous variation in rotor speed; of course, this variation is very small it is because of variable torque command, but practically rotor speed almost constant.
\nThe predictive DTC strategy has good tracking behavior and it is confirmed that the reduction in torque and flux ripples is achieved as there are absolute absence of over currents and reduced ripples in rotor currents as shown in \nFigure 4(f)\n. From the \nFigure 4(f)\n, it is noticed that there is continuous increase and decrease in the amplitude of rotor currents, similar to stator currents as shown in \nFigure 4(a)\n.
\nIn this Section, the performance of the proposed control scheme of DFIM has been investigated near the synchronous speed. This is examined by varying the speed of DFIM from 1580 rpm (hyper synchronous value) to 1340 rpm (sub synchronous value) in terms of sine wave with frequency of 3 rads−1 and phase shift of 90°, with the reference values of torque and rotor flux are set to 0.4 pu and 1 pu respectively.
\nEven when the speed command is varied suddenly from hyper synchronous value to sub synchronous value no much transient peaks occur in stator currents, as shown in \nFigure 5(a)\n, which clearly emphasizes there is reduction in over currents in stator and this is because of proper selection of switching sequence of rotor voltage vectors.
\n(a) Response of stator currents of DFIM with variation in rotor speed from 1580 to 1340 rpm. (b) Torque response of DFIM with variation in rotor speed from 1580 to 1340 rpm (i) proposed strategy and (ii) classical DTC. (c) Response of stator flux of DFIM with variation in rotor speed from 1580 to 1340 rpm. (d) Response of rotor flux of DFIM with variation in rotor speed from 1580 to 1340 rpm. (e) Rotor speed of DFIM with variation in rotor speed from 1580 to 1340 rpm. (f) Response of rotor currents of DFIM with variation in rotor speed from 1580 to 1340 rpm.
As shown in \nFigure 5(b)\n, the torque developed by the machine for proposed strategy and classical DTC and it closely follows the reference torque which means the dynamic performance of the machine is quite satisfactory but when the rotor speed nears the synchronous speed at around 0.5 s, variable torque ripple is produced. This variability in the torque ripple is due to continuous selection of zero voltage vectors at that instant. It indicates, the smaller amplitude of rotor voltage vector is required at the instant of rotor speed nearing the synchronism, which actually causes the reduction in torque ripple and this leads to degradation of quality of control. As shown in \nFigure 5(b)\n, the ripples are reduced in electromagnetic torque response.
\nStator flux responses are shown in \nFigure 5(c)\n. From the figure, it is observed that the stator flux remains constant and is not affected by change in rotor speed and also it is sinusoidal in nature.
\nThe response of rotor flux is shown in \nFigure 5(d)\n, from the figure, it is observed that there is no effect on the rotor flux due to sudden change in the rotor speed and rotor flux is also not affected by the changeover.
\nThe response of rotor speed is as shown in \nFigure 5(e)\n, when reference speed is varied from hyper synchronous value to sub-synchronous value and it is observed that the rotor speed response of DFIM follows the command speed.
\nSimilar to stator currents as shown in \nFigure 5(a)\n, no much transient peaks occur in rotor currents, as shown in \nFigure 5(f)\n, that is, there is reduction in over currents in the rotor, which is because of proper selection of switching sequence of rotor voltage vectors.
\nThe proposed control method makes two general contributions to the predictive control techniques. Firstly, it shows that using instead of two voltage vectors operating three appropriate vectors, allows operating at low constant switching frequency. Secondly, it is crucial to achieve the whole good performance of the DFIM, in terms of torque and current ripples by reducing the ripples of both directly controlled variables instead of only one.
\nFrom the proposed control method, it is possible to reduce the stress of the switching devices of the voltage source converter, in terms of low constant switching frequency behavior and switching power losses reduction, often demanded requirements in high power applications.
\nIt presents good tracking behavior, capable of working at variable speed operation conditions, for both motoring and generating modes at sub- synchronous and hyper synchronous speeds when compared to DTC technique, making this control suitable for applications such as wind power generation.
\nThe new DTC technique allows obtaining quick dynamic responses in respect to DTC method, with absolute absence of non-desired over currents in the machine. It ensures reduced torque and flux ripples, due to the control effect. The simulation results showed the effectiveness of the proposed method, to control the torque and the flux of the DFIM at considerably low constant switching frequency.
\nIn this Chapter, new predictive DTC has been developed for DFIM. The proposed control scheme uses two voltage vectors instead of three voltage vectors and it allows operating at low constant switching frequency and reduces torque and flux ripples, and also capable of working at variable speed operating conditions for both motoring and generating modes at sub-synchronous and hyper synchronous speeds compared to classical DTC technique. The comparison of torque and flux ripple values (difference of maximum to minimum ripple value) and its reduction given by the difference of maximum value and minimum value to average value is given in the \nTable 5\n below.
\nS. No. | \nParameter | \nClassical DTC scheme | \nPredictive DTC scheme | \n
---|---|---|---|
1 | \nTorque ripple | \n0.3 pu | \n0.01 pu | \n
2 | \nReduction in torque ripple | \n2.5% | \n1.25% | \n
3 | \nRotor flux ripple | \n0.018 pu | \n0.01 pu | \n
4 | \nReduction in rotor flux ripple | \n1.8% | \n1.05% | \n
Torque and flux ripple reduction comparison.
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He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"9",type:"subseries",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. 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