\r\n\tMethadone maintenance treatment (MMT) has become the main pharmacological option for the treatment of opioid dependence. Methadone remains the gold standard in the substitution treatment, which is a harm reduction intervention, because the patient does not become abstinent, but there are a series of positive changes. Currently, the surveillance of methadone substitution treatment is considered an ongoing challenge, given the need for the individualization and the increasing of the therapy efficiency. Methadone has been also studied as an analgesic for the management of cancer pain and other chronic pain conditions.
\r\n
\r\n\tThe complexity of methadone pharmacology, the high inter-individual variability in methadone pharmacokinetics, the risk of opioid diversion, the overdose and other adverse events pose many challenges to clinicians. \r\n\tThe aim of the proposed book is to update and summarize the scientific knowledge on the opioid dependence, including the mechanism of opioid dependence, the misuse of prescription opioids and the substitution therapy of opioid dependence.
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1. Introduction
The soil as the support of all terrestrial ecosystems is distributed as a continuous landscape and varies according to drainage, geomorphology and litho-climatic conditions [1]. There are five major factors that control the formation of soil: parent materials, climate, biota, topography and time [2]. These factors present interdependence; for example contrasting climatic regimes are likely to be associated with contrasting types of vegetation. Nonetheless in certain situation one of the factors had the dominant influence in determining differences among of set of soil [3]. In addition processes of soil genesis are operating under the influence of environmental factors; therefore we can understand the relationship between particular soils and the landscape and ecosystem in which they function [4]. Climate is perhaps the most influential of the four factor acting on large geographical areas (large scale), contributing to the development of specific types of soils and vegetation patterns [5], such as Gelisols (tundra vegetation: lichens, grasses and low shrub), Histosols (water loving plants: pond weeds, cattails, sedges, reed, mosses), Spodosols (mainly coniferous species), Alfisols (deciduous forest), Mollisols (grasses), Aridisols (xerophytic plants), and Oxisols (tropical rain forest). However, at regional level (medium scale) soil variability is often related to small changes in topography and thickness of parent materials or to the effect of organism. In Trans-Mexican Volcanic Belt, is common finding soil sequences or catenas related with specific vegetation species such as Andosols (Pines-volcanic ashes), Cambisols (deciduous trees-colluvial material), fluvisols (crop lands-alluvial material) y solonchack (halophyte grasses-lacutrine material) [6]. Finally at local scales, variation in edaphic characters often provides the best statistical explanation for variation in floristic composition. Frequently, systematic variations in the parent material are closely related to endemism [7]. Endemic species have relatively narrow tolerance to changes in their environment, and can be dependant on certain geologic and edaphic features. There are studies that provide detailed information on species and plant communities, some include relief features, and others report physical and chemical analyses which allow us to infer about soil fertility [8,9] while some other studies have performed statistical analysis to correlate such variables [10-12]. This chapter presents a revision of the current knowledge on the role of soil properties as for pH, H2O2 reaction, and reactions to HCl in the distribution of endemic plant species and synthetize in table 1 studies that give details examples of this relationship (plant-soil endemicity).
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tSpecies/References\n\t\t\t
\n\t\t\t
\n\t\t\t\tSoi/Substrate type\n\t\t\t
\n\t\t\t
\n\t\t\t\tTechnique\n\t\t\t
\n\t\t\t
\n\t\t\t\tResults/Conclusions\n\t\t\t
\n\t\t\t
\n\t\t\t\tLocalities\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tCalochotus\n\t\t\t\tobispoensis, C. tiburonensis, C. pulchellus (Liliaceae) [40]
Sandy soils, gravelly soils and quartzitic outcrops
\n\t\t\t
Each 25 m2 quadrant was classified according to soil types and were sampled for chemical and granulometric analyses
\n\t\t\t
The spatial distribution of Coccoloba was largely related to the arrangement of sandfields
\n\t\t\t
Serra Do Cipó, southeastern Brazil
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tErigonum nervulosum (Polygonaceae) \n\t\t\t\tStreptanthus brachatus and S. morrisonii (Brassicaceae) [76]
\n\t\t\t
Serpentine barren soils
\n\t\t\t
Low concentrations of Ca, Mg, P, N
\n\t\t\t
Differences in Ca and Mg between serpentine soils allow distinct species distribution \n\t\t\t
\n\t\t\t
Lake County, California, USA
\n\t\t
\n\t
Table 1.
Studies in which species distribution and soil features are reported.
2. Endemism
Endemic, in botany, means that a plant species is considered native to the country –region-where it can be found [13] and the term is applied to the distribution of organisms [14]. Although climatic factors are the most studied [15,16]; endemism is a non-ecological [17], geological event. Climate limits the flora [18,19], while geological characteristics largely define habitat diversity [4]. Moreover, edaphically severe habitats commonly support edaphic endemics, which are plant species that do not occur elsewhere [20]. Although that might be not enough to recognize the endemic species, edaphological characters (macro and micro) are essential to establish phylogenetic hypothesis of endemic taxa and areas of endemism, looking for consistency with geological models [21-23].
3. Soil
Soil, in soil taxonomy [24] is a natural body comprised of solids (minerals and organic matter), liquid, and gases that occurs on the land surface. Soil occupies space, and is characterized By one of the following: horizons or layers, that are distinguishable from the initial materials a result of additions, losses, transfers, and transformations of energy and matter or the ability to support rooted plants in a natural environment.
We intend to clarify that for some plant species, soil has played an essential role in their evolution and current distribution. Therefore, it is necessary to indicate that according to soil taxonomy is not possible to classify the earthy materials used in pots in greenhouses. In the same sense, plants even grow on trees, but trees are regarded as non soil. Soil covers the earth’s surface as a continuum, except on bare rock. Some endemics are restricted to a particular geological formation or to one type of rock or rock outcrops. Endemicity found on rocky outcrops, either calcareous or otherwise, has been reported by several researchers [25-31].
In order to understand the relevance of soil for plant endemism, it is necessary to highlight that the geological processes as genesis of unique soil types may provide the necessary isolation for the genesis of unique biota, and the edaphic factors are used to draw the link between environments and taxa [32]. If the environmental scenario is potentially multi-dimensional [33], some soil properties should be also considered as predictors. Previous research [2] considers that the distinction between soil and environment is arbitrary; and might constitute a theoretical artifact which does not represent natural processes.
3.1. Soil genesis
The transformation of rock into soil is designated as soil formation. Climate, organisms, relief, rocks, and the time are soil forming factors. Therefore, soil can be considered as a particular combination of its forming factors. For a given combination of factors there is only one soil type [2]. Soil properties such as pH, clay content, porosity, etc, are determined by the combination of these factors. The smallest change in any one of the properties, gives rise to a new soil.
Climate is usually considered the dominant soil forming factor, and cannot be described by a single index [4]; for example, the high proportion of smectite in soil, indicate a highly seasonal semiarid subtropical climate [34].
Living organism as bacterial species are able to fix N2, dissolved P, weathered extrusive igneous rock, marble, and limestone, and significantly mobilized useful minerals, such as P, K, Mg, Mn, Fe, Cu, and Zn in rock minerals [35]. Additionally, plant root systems alter the structure of the surrounding soil [36], and the roots of some plants have the ability to exude low-molecular-weight organic acids that produce changes in the availability of nutrients [37].
Relief modifies the water relationships in soils, affected by slope processes such as erosion, landslides and other mass movement [38-39]. However, little is known about the dynamics of soil mosaics formed by slope processes; whereby mountainous regions are characterized by high soil diversity.
The soil formation is regulated by the origin of parent material and by the age of the exposed surface. The nature of the parent material profoundly influences soil characteristics such as the chemical weathering and the quantity and type of clay minerals. While the total composition of the parent rocks is only one of the factors involved in soil formation, it is of considerable interest to analyze some of these rocks, and of soils of similar origin. The time of soils formation refers to the age of the exposed surface. The soils change with time and undergo a process of evolution [2].
3.2. Soil properties
Physical, chemical and mineralogical analyses are used in soil taxonomic criteria. For standard laboratory methods descriptions see Appendix of Keys to Taxonomy of Soils [24]. The diversity of soil properties resulted in the diversity of soil use and soil ecological functions [38,39]. In edaphic islands such as serpentine and limestone outcrops upon which plant grow are necessary specific analyses; per example, in ultramafic soil trace elements as Mn, Cu, Zn, Cr and the heavy metals Ni and Co are extracted, as well as in calcareous soils are measured soluble and exchangeable P [40,41]. Overall, previous authors agree that vegetation differences are strongly associated with differences in the bedrock [29,42].
4. Sampling approach
The existing literature comprises a wide range of sampling techniques to obtain vegetation samples. The plant populations may vary in size and in number of individuals per species. According to our field observations, it is necessary to select populations that adequately represent the endemic area (Figure 1). Soil sampling must be performed in the same locations where vegetation has been previously sampled. The overlap of both sampling activities allows correlating changes in vegetation and soil [43]. Thus, the resulting plots are clearly representative of the surrounding area. Accordingly, geology and topography are essential characteristics that need to be thoroughly examined. For instance, spatial variability studies have indicated that even when the relief of the site is gently rolling, erosion processes can affect soil properties [44, 45], and therefore alter the results of sample analysis. We do not recommend to combined soil samples from which the different edaphic variables are measured [11], this method can give erroneous conclusions because information at the micro-scale level is lost.
Figure 1.
Delimiting study area in slope or plane environments. (A, B) Nuevo León, México. Habitats for several succulent species endemic to the Chihuahuan Desert.
The geology and the topography are factors that influence the formation of particular soil type and the establishment of specific biological forms, [38]. The geological origin of rocks can be identified visually and mineralogical composition by X-ray diffraction or other methods [31]. Undisturbed rock samples must be collected, without showing any chemical or physical weathering. The slope and rock outcrops are some landscape features easy to distinguish. The slope is important by the sediments mobilized by slope processes as for landslides, colluviation, and accumulation of the material that eroded from upper landscape positions [39], this can be recorder in degrees or percent. The geomorphic position can register as summit, shoulder, back slope, toe slope, and floodplain if any (Figure 2).
Figure 2.
Geomorphic position of Cephalocereus species. (A) C. nizandensis on summit; (B) C. senilis on summit and shoulder.
The slope aspect would influence insolation, temperature and moisture. Isolation, temperature and moisture also must be into accounted because they have effect on the bedrock or with the vegetation [29, 77]. In the surface it is necessary to test soil and bedrock reactions to HCl as a measure of their calcareousness. Moreover, the soil properties of depth, stoniness, root distribution, H2O2 reaction, structure, and Munsell color should be recorded [31,54]. If the characteristics of the site permit, three or more bulk soil or rhizosphere soil and rock samples along the geomorphic position per site should be collected (Figure 3). In the cases where there was no soil, organic debris must be collected [54].
Finally, the abundance of certain minerals in the soil can influence the physiological response and metabolism in plant species, such as heavy metals accumulation and the synthesis and accumulation of biominerals [40, 53]. The most common biominerals in plant tissues are calcium oxalates (Figure 4), and their abundance is associated with calcareous soils [78]. We recommend isolate crystals from the plant tissue for better analyses and carried out X-ray diffraction (XDR), chemical composition and morphology with scanning electron microscopy (SEM). We also suggest use the petrographic microscope to know the optical properties of the crystals for be able to identify in the sand fraction of soil [57]. Additionally to relate the soil elements that plants take up soil and incorporate in their tissues, use energy dispersive X-ray (EDX) on crystals analyses. In order to determine the importance of the biominerals in the soil properties, is necessary that at least three hundred grains from the sandy fraction will count on a grain mount by line counting method using a petrographic microscope [54, 57].
Figure 3.
Samples. (A) rock and organic debris for Cephalocereus apicicephalium; (B) soil for C. totolapensis.
Figure 4.
Calcium oxalate crystals isolated from Cephalocereus species. (A) petrographic microscope; (B) scanning electron microscopy.
5. Edaphic endemism
The interest for environmental conservation has been growing over the last decades. However, there has been little or no consensus on priority species or conservation strategies Table 2. The conservation of soil diversity greatly overlaps with such for plants, and both become endangered as a result of land use [46]. The fact that soil taxa are geographically restricted is important for planning and conservation efforts. Soil characteristics often play an essential role in determining plant community distributions [47]. The endemism of native plant species in edaphically specialized habitats suggest that these native endemic species are uniquely specialized to survive and grow better under the conditions prevalent in these harsh areas. For instance, several authors [48] reported there are almost five edaphically-restricted or -endemic butterflies, mostly associated with serpentine soils. Some species are absolutely limited by the edaphic restriction of their host plants. These are a better argument for biodiversity preservation [49].
Studies in which species endemism are related to soil features.
Some explanations for the close relationship soil-plant have been looking at morpho-anatomical changes and physiological response to variations in soil parameters. Soil nutrient status determines leaves with glands or without glands: sclerophyllous (leaves without glands) plants exist almost exclusively on oligotrophic soil; whereas orthophyllous (leaves with glands) growth on more or less equally on both oligotrophic and eutrophic soil [50]. In this sense, exceptionally high levels of species turnover were found along all three soil fertility gradients which reflect the high degree of edaphic specialization of the flora [51]. Soil fertility is difficult to quantify, because it dependent not only of the nitrogen (N) and phosphorus (P) status of the soils, but also on their availability. Differences between species in ability to solubilize mineral nutrients could affect the ability or inability of plants to grow in particular soils. In calcareous soils, species suffer lime-chlorosis by Fe deficiency, and their growth is affected by inability to solubilize the native phosphate [52]. In ultramaphic soils, the vegetation accumulates large quantities of heavy metals in their tissues. So, endemic plants have developed strategies to grow successfully in these unusual conditions. For example, to minimize water requirements and excessive water loss, serpentine plants are able to reduce water potentials to levels lower than found on nonserpentine soils, as well as keep stomata closed or nearly closed [53].
Soil constitutes the main source of nutrients for plants. However, for plants growing on bare rock mycorriza have been described as an important factor in promoting edaphic specialization [35, 37]. This high degree of host specificity of symbiotic microbes could enhance nutrient uptake in the infertile soils or rocks. In rhizoplane of cacti, several bacterial species were isolated. This bacterium fixed N2, dissolved P, weathered extrusive igneous rock, marble and limestone, and significantly mobilized useful minerals, such as P, K, Mg, Mn, Fe, Cu and Zn in rock minerals [35]. Other rock-colonizing cacti usually grow in cracks or fissures that are deeply penetrated by the root system: Mammillaria fraileana [31] and Cephalocereus apicicephalium and Cephalocereus nizandensis (Figure 5) [54]. Little is known about weathering mechanisms, except that the roots of these species can exude low-molecular organic acids (LOAs). The LOAs in root exudates may play an important role in the solubilization and plant availability of mineral nutrients in the rock [55]. Additionally, [20] mentioned that rock outcrop represent refuges from competition with other (often exotic) species.
Figure 5.
Rocky habitats. (A) Cephalocereus apicicephalium grows in fissures; (B) C. nizandensis grows in cracks.
The edaphic endemics are now restricted to unusual and sometimes contaminated soils, but may have been able to withstand large concentrations of metals in their tissues or large quantities of calcium oxalate crystals [40]. The ability to tolerate excessively high levels of nickel and other heavy metals may be a physiological adaptation of the genus Calochortus and not necessarily an evolutionary response by several species to life on an ultramafic substrate. The amount of crystalline Ca oxalate in the oldest leaves of Eucalyptus diversicolor may be related in part to the high levels of exchange-able soil calcium [56]. Cephalocereus species could accumulate great quantities of calcium oxalate crystals even if there is low calcium soluble in the soil [54]. Cephalocereus nizandensis and C. apicicephalium grow on limestone outcrops, where the Ca is precipitated and C. totolapensis preferred acid soils from andesites, siltstones or mica schist (soluble Ca is 19-72 parts per million). The amount of soluble Ca is also very low in where grow C. columna-trajani (63-229 parts per million) and C. senilis (82-100 parts per million) [57]. The last two species have the larger epidermal crystals of the genus (Figure 6) [58].
Figure 6.
Prismatic calcium oxalate crystals in epidermal cells. (A) Cephalocereus columna-trajani; (B) C. senilis. Scale A = 40 μm, B = 50 μm.
For studies of evolution of a soil-type adapted endemic species is necessary to perform phylogenetic analysis. The existence of genetically compatible taxa with such distinct edaphic requirements presents a unique opportunity for intensive study of the genetic basis of tolerance to soil-type. A group plant may have evolved in a very dynamic selective context with strong edaphic selective pressures. In [59] they examined phylogenetic relationships of the rare serpentine endemic taxon Caulanthus amplexicaulus var. barbarae. They found that the serpentine taxa were nonmonophyletic evolving independently at least three times, suggesting that tolerance to serpentine may be gained or lost through relatively few genetic changes. In other case, [50] construct phylogeny for the Pentaschistis clade with 82 species in three genera. They investigated the association between leaf anatomy type and soil nutrient type on which species grow. Despite there is little phylogenetic constrain in soil nutrient type. However, only orthophyllous-leaved species diversify on eutrophic soils. Nevertheless, modern phylogenies on a number of phanerogam genera occurring on New Caledonia (Acianthus, Cupaniopsis, Guioa, Morinda, and Oxera) have shown a shift in soil preference (from non serpentine to serpentine soils and vice versa). Thus [60] concluded that the ability to grown on serpentine soil is either a plesiomorphic or a very homoplasious character and therefore the hypothesis that serpentine soils preserve the indigenous flora in New Caledonia against competition with immigrant species cannot be supported for these groups. Rarely are made specific soil studies, the data are taken often of general charts. We suggest making detailed studies as in Cephalocereus, according to plant species and soil type [57]. Therefore, it might be possible to infer the role of soil in the evolution of endemic plant species using the phylogenetic analysis.
6. Conservation strategies
In the landscape, the vegetational differences often serve to delineate the geologic discontinuities of an area even to the casual observer. The remarkable differences often observed in plant cover for different soil types in adjacent areas, have naturally led to attempts to explain these phenomena in terms of the physical or chemical properties of the soil, or of the physiological characteristics of the plants [61]. These areas should be priority sites for conservation to preserve the unique interaction between soil and plant species as well as the microbiota and fauna. For example, the halophytic and gypsophytic vegetation of the Ebro-Basin at Los Monegros [62] or flora of the Coastal Calcareous Hills of the Biosphere Reserve Baconao in Cuba [63] are excellent to demonstrate the varied adaptations of plant types and life-forms as strategies to survive on edapho-climatic harsh conditions of various kinds. In the Chihuahuan Desert region it was found that several Cactaceae species, particularly many members of the Cacteae tribe often inhabit extremely specialized habitats, such as gypsum and other unusual soil formations (Figure 7) [64]. The patches of edaphic endemism also frequently exist as refuges for native species in highly invaded ecosystems [20], ultramafic substrates act as sites in which Pinus balfouriana escapes of the competition [65]. Moreover, the work with Helianthus exilis showed the need to protect specialized microhabitat found only within the large serpentine outcrops, the species cannot survived outside the narrow conditions proper of its habitat [66]. However, the scarcity of conclusive studies on role of soil to determine the prevalence of endemic plants hampers the efforts of public and private organizations to preserve such areas.
Figure 7.
A) Aztekium ritterii grows in outcrops of steep slopes of crystalline grypsum; (B) Turbinicarpus valdezianus grows in calcarious rocks.
The population size can greatly vary among populations of the same specie generating micro-endemic nature. The small populations of some species consist of adult individuals that may be 3 as in Cephalocereus totolapensis [54] or 4 as in Guaiacum unijugum [67]. These population sizes are not reported sometimes considerer no significant, but many regions show a unique assemblage of species or a higher level of species richness or other associated species which could serve to protect this ecosystem. In Australian alpine vegetation the analyses of the relationships between physiognomic variation and environment indicate that edaphic factors are more important than climatic factors in differentiating formations [18]. Thus, edaphic discontinuities should be determining the size and population distribution and should be considered when proposing conservation areas.
Frequently, endemic species are less widely distributed and are less well represented in protected areas than other threatened species [68]. Baconao Biosphere Reserve presents high floristic composition and which endemic species represent 21%; however they are restricted at limestone hills, parent material that cover only 6.6% of total area [63]; and the same applies when registering Asteraceae endemic species to the Mexican state of Oaxaca with 53.4% and many of them not found in any Biosphere Reserve [69]; in the same sense, 12 endemic plant species are restricted to serpentine soil in Puerto Rico, all rare, uncommon and very localized within their limited distribution, and only two have been placed on the United States Federal list as threatened or endangered [70], and most species endemic to the state of Nuevo Leon (Northeast of Mexico) present in the submontane scrub have restricted distribution and specially cacti are not located within any protected area [71]. Therefore it is important to reconsider the extension of the sites identified as irreplaceable for various members of the flora endemic worldwide.
The effects of growing human populations on natural communities, on ecosystems, and on some endemic plant populations results in degraded state of sites due to human activity as roadway, tourist development, extraction of mineral as gravel, sand, and others. However, an understanding of the interrelations between soil or bedrock and occurrence of endemics, becomes even more important in the context of restoration ecology and the reversal of land degradation. In addition, the role of soil in the determination of endemic plants has not been sufficiently studied; thus, public and private organizations have not intensified their efforts to preserve such areas. Table 1 show some examples of the different soils supporting endemic species and how through different techniques of study has been able to establish a close relationship between soil and plant. As can be seen, the soil-plant endemism is not exclusive to one type of soil or a plant family.
7. Conclusions
Diverse studies have demonstrated that soil characteristics are correlated with differences in bedrock. Although incipient and timid studies intend to respond to which extent do soil characteristics correlate with vegetation patterns, this is a question that should not be forgotten in plant endemic studies.
We cannot do random sampling hoping to find a relationship between habitat and population size or presence / absence of some species. Efforts should be directed to characterize the habitat or habitats where the species grows in order to determine whether the type of soil, rock, and bedrock are the most important factors for endemic species development. When the limiting factor is the substrate, and abrupt limit in abundance is expected, as well as in population parameters. Narrow endemism in plants is frequently related to soil specificity, and many endemic plants are found in patches of certain soil within a different soil matrix. Additionally as with the soil, vegetation usually changed abruptly at the contact zone.
Edaphic endemic plants are highly vulnerable to extinction due to stochastic events, habitat degradation, climatic change, and invasion by weedy species. Finally, the protected areas may be unable to maintain regional species diversity and representativeness, especially if additional fragments are lost and fragmented landscapes are left unmanaged and they are fragile to soil erosion or degradation by chemical contamination.
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Introduction",level:"1"},{id:"sec_2",title:"2. Endemism",level:"1"},{id:"sec_3",title:"3. Soil",level:"1"},{id:"sec_3_2",title:"3.1. Soil genesis",level:"2"},{id:"sec_4_2",title:"3.2. Soil properties",level:"2"},{id:"sec_6",title:"4. Sampling approach",level:"1"},{id:"sec_7",title:"5. Edaphic endemism",level:"1"},{id:"sec_8",title:"6. Conservation strategies",level:"1"},{id:"sec_9",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Cotler AH. El Uso de la Información Edáfica en los Estudios Ambientales. Gaceta Ecológica 2003;(68) 33-42.'},{id:"B2",body:'Jenny H. Factors of Soil Formation. A of System Quantitative Pedology, New York: Dover Press;1994.'},{id:"B3",body:'Boul SW, Hole FD, McCraken RJ: Génesis y Clasificación de Suelos. México: Trillas; 1998.'},{id:"B4",body:'Brady NC, Weil RR. The Nature and Properties of Soils. New Jersey: Prentice-Hall; 1999.'},{id:"B5",body:'USDA-NRCS.Global Soil Regions 1:1 million scale. 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Naturwissenschaften 2006;93(3) 114-118.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"María Luisa Bárcenas-Argüello",address:"marialuisabarcenas@gmail.com",affiliation:'
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1. Introduction
In recent times, potential energy, environment, and economic interests have stimulated motorized industry to develop and enhance efficient, clean, and sustainable vehicle, particularly, for city transportation. This new invention should not contingent on oil as a sole of energy source. Additionally, reducing engine size, replacing mechanical components by electrical devices, transferring request information electronically instead of mechanically, and designing integrated control systems are considered other targets that automotive manufactures are aiming to attain in producing new means of transportation. At that juncture, the automotive industry introduced electrical vehicle (EV), which is driven by alternative energy sources that provide magnificent means for efficient, clean, and environmentally urban transportation.
The trend technology toward electronic components and circuits coming from their technical merits not only reduces the weight of vehicles but also has the potential for a large number of integrated functions and features. Some of these new electronically operated systems are taken place under the concept of x-by-wire, which involves brake-by-wire, throttle-by-wire, and steer-by-wire. These electrical vehicle subsystems yet still undergo considerable challenging issues that need intensive study and investigation in order to find out appropriate design and powerful operated system.
This chapter presents x-by-wire technology implementation in electric vehicle. BBW is a new brake technology in which mechanical and hydraulic components of traditional brake systems are replaced by electric circuits and devices to carry out the function of braking in a vehicle by wire-transmitted information. The advantages of electronic devices such as reducing vehicle weight and increasing brake performance are considered the main purpose trends of the automotive industry toward this new brake technology. Another application known as n EPAS system is a driver-assisted feedback system designed to boost the driver input torque to a desired output torque causing the steering action to be undertaken at much lower steering efforts. Particle swarm optimization (PSO) algorithm is implemented as tuning mechanism for fractional-order PID (FOPID) controller. The aim of this controller is to track the assist current generated by lookup table. The results show the performance and efficiency of using PSO algorithm for FOPID tuning.
The motivation of this study is to enhance the safety aspects for the vehicle while attaining any desired speed. To achieve that, an optimal brake force at different road types and conditions and for different brake commands must be obtained within a reasonable time and without vehicle sliding.
2. BBW design and principle of work
The proposed design of BBW used in this study is schematically illustrated in Figure 1, which includes one wheel of vehicle model as seen inside the dotted box. According to the figure, the suggested principle of work of BBW is adopted which is demonstrated as follows:
Figure 1.
Proposed BBW architecture for one-wheel brake model.
Primarily, reducing (or halting) vehicle speed comes as a result of pressing down on the brake pedal by the driver. The braking pedal of BBW is usually equipped with several electronic sensors that provide redundant information about braking request. Thus, when a brake force applies to the brake pedal, three possible sensors are usually utilized to measure required braking force: (1) pedal displacement sensor (measures pedal displacement as a result of applying force on the pedal) [3], (2) force sensor (measures applied force on brake pedal), and (3) pressure sensor (measures applied pressure to brake pedal) [5]. In addition to that, the brake pedal of BBW may not necessarily be as the general brake device, rather than it could be a hand-adjacent device placed at the steering wheel that enables driver to apply brakes with hand movement as suggested in [5]. However, since the focus of this study is to design control strategy for braking action, it is assumed that braking request is already measured and available in the form of voltage source as adopted by Mingfei’s design [1]. Therefore, the chosen voltage source of this study exists within a range of 0–5 V in which 0 V relates to released brake pedal and 5 V relates to fully pressed pedal. This voltage range is formulated in such a way that brake pedal input, which is in the form of voltage source, matches the desired vehicle speed by using 1D lookup table as shown in Figure 2. This lookup table enables a range of inputs that correspond to [0–5] Volts which in turn this range corresponds to the vehicle speed range [0–100] Km/h; for example, if the input brake pedal corresponds to 2.5 V, the relative required vehicle speed will be 50 Km/h as illustrated in Figure 2(b). Nonetheless, the relative values of vehicle speed are changeable according to initial vehicle speed (vehicle speed before braking action), whereas voltage range remains constant all the time and has the capability to correspond to any given vehicle speed by updating vehicle initial speed. For instance, if the initial vehicle speed is set to 50 Km/h, the voltage range [0–5] V will correspond to the vehicle speed [0–50] Km/h as explained in Figure 2(b).
Figure 2.
Lookup table of the input brake force. (a) Tool box. (b) Vehicle speed-voltage source.
Upon determining the required brake request, the braking command is then sent to the control unit (CU) via wires as shown in Figure 1. The CU located at the wheel after that determines exactly the control signal that must be transmitted to the brake actuator unite in order to slow down or stop the vehicle. Nevertheless, the control signal of the CU is considered the input for the electrical actuator (permanent magnetic DC motor) where this signal takes the form of the desired braking torque. Consequently, electronic actuator of the brake unit operates based on the desired braking torque which in turn decreases (or stops) vehicle speed according to the desired speed.
The control unit, however, is updated through feedback control strategies where wheel speed is considered the input to the feedback control system according to applied control strategy. Moreover, the interaction between brake pedal, control unit, electronic actuator, and wheel as well as vehicle speed is completely accomplished by wires. In view of that, vehicle brake system is designed and structured.
3. Control system design
The suggested control brake system employs fuzzy-PID controller to obtain the desired vehicle speed based on tuning of traditional PID controller. The applied control algorithm must be able to function to any required vehicle speed that is determined by the driver. The proposed control system design applied to handle this task is schematically illustrated in Figure 3.
Figure 3.
BBW control system design.
As depicted in Figure 3, the system input (brake pedal force) is determined by the driver in the form of voltage signal ranging from 0 V (refers to release pedal) to 5 V (refers to fully pressed pedal). Upon determining the required vehicle speed by lookup table, the speed signal is then sent to the control unit which is based on either PID or fuzzy-PID controllers. The implemented control algorithm then determines the desired voltage source that must be transmitted to electrical actuator in order to generate required braking torque. The wheel speed after that is decreased by applying brake torque causing modification in overall system dynamics which in turn leads to vehicle speed deceleration.
The error signal transmitted into control algorithm, however, is determined by the difference between input signal (desired vehicle speed) and feedback signal (wheel speed) which is given by the following relationship:
The control strategy used to deliver the desired vehicle speed is based on maintaining peak slip ratio within the maximum adhesion characteristic range [0.02–0.35]. Locating peak slip ratio within the maximum friction characteristic initiated from applying ideal and accurate brake torque is capable of deriving proper and acceptable vehicle-wheel speed relationship.
The control objective of both controllers is to decrease vehicle velocity to the desired vehicle speed (5 Km/h) while maintaining slip ratio within its maximum range [0.02, 0.35]. Besides, the control algorithms are designed to operate braking action on dry asphalt road type, whereas other road types and conditions (such as wet asphalt, wet and dry cobblestone, and concrete) are applied to examine and investigate whether the controllers can handle characteristic variations of the system or not.
a) PID controller design
A cascade-form PID controller is designed based on manual tuning method, where the three terms of PID controller (proportional, integral, and derivative) are employed. Accordingly, the overall controller output is considered the sum of the contributions of the individual PID terms which is further expressed in Eq. (1), where ut is the PID control signal, et is the error signal, and Kp,Ki,Kd are the proportional gain, integral gain, and derivative gain, respectively.
ut=Kpet+Ki∫0teτdτ+KdddtetE2
b) Fuzzy-PID controller design
Although PID manual tuning method provides stable output response, PID controller does not achieve the desired control specifications since the dynamics of the system has nonlinear and variant parameters which in turn degrade system performance. Therefore, fuzzy logic controller has been introduced to PID controller in order to improve the response as well as to enhance system performance based on fuzzy-PID tuning. In fact, fuzzy-PID controller is considered as a link between traditional control which has well-established theory and intelligent control that conquers traditional control problems like nonlinearity.
Fuzzy-PID scheme, in addition, can employ different structures and forms based on the input to the fuzzy controller on the one hand and on the arrangement of PID parameters and their locations with respect to fuzzy controller on the other hand. Nonetheless, these different structures are possible in the context of knowledge description and explanation, whereas they should be examined with respect to their functional behavior. The proposed structure of this study is schematically illustrated in Figure 4, which generates incremental and absolute fuzzy-PID signal based on direct action to tune PID parameters through fuzzy inference.
Figure 4.
Fuzzy-PID controller (MATLAB Simulink scheme).
As shown in Figure 4, the error and rate change of error are considered as the time-varying inputs to the fuzzy logic controller (linguistic inputs), whereas tuned (proportional, integral, and derivative) gains are the output of the controller (linguistic outputs). Regarding linguistic inputs, there are other choices (such as integral of error) that could also be used as input variables, yet the selection variables make good intuitive sense, particularly as the input error is naturally engaged in the control problem of regulating process output around specific set point. The controller input variable however must have proper information available to provide good decision to derive vehicle speed into the desired speed to achieve high-performance operation based on fuzzy-PID tuning. On the other hand, the linguistic output variables are expressed as tuned (proportional, integral, and derivative) gains where the output values of these tuned gains are implemented to tune the conventional PID controller as shown in Figure 4.
The adopted linguistic values and their corresponding abbreviations in conjunction with their linguistic variables are summarized in Table 1.
Linguistic variables
Linguistic values
Linguistic value abbreviation
Input
Error
Positive, zero, and negative
P, Z, and N, respectively
Rate change of error
Output
Proportional gain
Positive, zero, and negative
P, Z, and N, respectively
Integral gain
Zero, positive small, and positive large
Z, PS, and PL, respectively
Derivative gain
Zero, positive small, and positive large
Z, PS, and PL, respectively
Table 1.
Linguistic variables alongside their linguistic values and abbreviations.
This table provides a language to express the control decision-making process in the context of established input–output framework. For example, the statement “error is negative” can be referred to the situation where the vehicle speed curve exists above the desired speed and needs more braking force. In contrast, the statement “error is positive” can be referred to the situation where the vehicle speed curve exists below the required speed curve and needs to decrease applying torque to obtain the desired vehicle speed.
Upon determining linguistic quantification, the rule base of the control system is set to capture expert’s knowledge on how to tune the system and describe applied control strategy. Since there are two input variables and three output variables, the possible rules can at most reach to 32 (9) rules. These rules are listed in a tabular representation form as shown in Tables 2 and 3.
Error
Change in error
N
Z
P
N
N
N
Z
Z
N
Z
P
P
Z
P
P
Table 2.
Fuzzy rule base for proportional gain.
Error
Change in error
N
Z
P
N
Z
Z
PS
Z
Z
PS
PL
P
PS
PL
PL
Table 3.
Fuzzy rule base for integral and derivative gains.
The meaning of the above linguistic description is quantified via membership function, whereas triangular shape is considered in this study for all inputs as well as all outputs for its simplicity, linear grade distribution, and fairly limited availability of the relevant information about the linguistic terms. In due course, the selected membership functions and their associated universe of discourse as well as linguistic values of this study are revealed in Figure 5. The designed membership functions are overlapped, and the height of the intersection of each two successive fuzzy sets is ½.
Figure 5.
Membership functions and their corresponding values. (a) Membership functions and their values for error input et. (b) Membership functions and their values for change of error ∆et. (c) Membership functions and their values for proportional gain. (d) Membership functions and their values for integral gain. (e) Membership functions and their values for derivative gain.
Since a clear picture on the linguistic variables, rule base, and membership functions have been explained, we move to the important issue of how the exact fuzzy controller works. In doing so, the first component of fuzzy controller is fuzzification process which is the act of acquiring the value of the input variable and defining numeric magnitudes for the membership function that are set for that variable. After that, the inference mechanism takes the action through two steps:
Matching the premise associated with all the rules to the controller inputs to determine which rules apply to the current condition. In other words, each rule in the rule base has different premise membership functions on the one hand and function of error and change in error on the other hand; therefore, the quantification of the certainty that each rule base applies to the current condition can be obtained upon providing specific values for the error and change in error.
Determining the conclusion (what the control action to take) that should be applied by using selected rules to relate to the current situation. This conclusion is classified with a fuzzy set that signifies the certainty that the input to the plant should undertake various values.
Therefore, as long as the input to the inference process (set of rules) is on, its corresponding output operates which is in the form of implied fuzzy sets. However, these implied fuzzy sets are then converted to crisp values (numeric values) by combining their effects to give the most certain controller outputs. The defuzzification process is obtained by bisector method which divides the area by a vertical line into two equal subregion areas. In addition, the mean of maximum and the largest of maximum are also applied to the system for the purpose of validity in which both of them provide close output result.
4. Simulation result and analysis
The vehicle model and controller algorithms are examined in MATLAB software. For the results of investigation and analysis, the initial vehicle-wheel speeds are set to 100 Km/h, whereas the desired vehicle speed is set to 5 Km/h. The reason of choosing 5 Km/h as the desired vehicle speed instead of zero Km/h is because slip ratio magnitude goes to infinity as vehicle speed approaches zero which in turn leads to inappropriate output behavior. On the other hand, selecting the desired low speed helps to examine maximum slip ratio that controls algorithm derives; hence, the ability to evaluate control performance and output response of the system will be more effective and visible.
The output responses of fuzzy-PID controller for dry asphalt road type are presented in Figure 6, whereas Figure 6(a) demonstrates the output responses of vehicle-wheel, and Figure 6(b) shows the output responses of slip ratio. Yet, the output responses of traditional PID controller are imposed in the same figure (Figure 6) to illustrate the comparison between traditional PID controller and fuzzy-PID controller.
Figure 6.
Output responses of fuzzy-PID controller for dry asphalt road type. (a) vehicle-wheel speed. (b) Slip ratio.
As shown in Figure 6(a), both controllers could derive stable output response smoothly. However, the output performance of the fuzzy-PID controller is much better than conventional PID controller since PID controller derives large steady-state error on the one hand and takes long time (approximately 15 seconds) to approach the desired vehicle speed (5 Km/h) on the other hand. In contrast, fuzzy-PID controller overcomes these problems being provided better output performance with zero steady-state error. As a result, fuzzy-PID controller could obtain the required vehicle speed within approximately 9 seconds which in turn assists to reduce stopping vehicle time 60% as compared to PID controller and more importantly the ability of fuzzy-PID controller to eliminate steady-state error to zero. Therefore, fuzzy-PID controller shows superior and outstanding controller.
On the other side, the output response of slip ratio associated with vehicle-wheel speed as shown in Figure 6(b) reveals smooth output response particularly before attaining the desired output speed. As depicted from the figure, the maximum slip ratio is the same for both controllers which approximately equals to 0.027. Though the maximum slip ratio magnitude seems a small value, the main cause for vehicle-wheel deceleration is considered since friction force between road surface and wheel surface principally depends on the slip ratio magnitude even though if the slip ratio possesses very small magnitude that may reach to mili-slip ratio.
This fact is clearly observed from Figure 6(b), especially within the time interval [2, 4] seconds, where the slip ratio of fuzzy-PID controller output response (blue line) has larger magnitude than PID output response (red line) by mili-values. This slight divergence that fuzzy-PID created, by trivial increases in slip ratio magnitude, leads to dramatically improve and enhance output response by decreasing vehicle stopping time 60% as compared to conventional PID controller.
As shown in the table, the slip ratio increases as the adhesion characteristic decreases. For instance, the maximum adhesion characteristic of dry asphalt is about 1.18 which is considered a large value; hence, its magnitude-derived slip ratio is small (0.027 for PID controller and 0.028 for fuzzy-PID controller). In contrast, the wet cobblestone adhesion characteristic has a small value (0.34), and therefore its derived slip ratio has a large value (0.26 for PID controller and 0.33 for fuzzy-PID controller).
The other significant notice that can be observed from Table 4 is the maximum slip ratio of fuzzy-PID controller which is slightly larger than the one derived by PID controller, meanwhile fuzzy-PID performance is considered superior and much better than PID performance as demonstrated above. Accordingly, the slip ratio magnitude is considered extremely important in braking operation even though if it possesses very small value since braking process depends on the road-wheel surfaces. Nonetheless, a certain magnitude range of slip ratio is permissible where if its magnitude exceeds that range, the operating system may undergo unwanted behavior (wheel locks up or losses the control) particularly if it goes to a large value (more than 0.5).
Road-type controller-type
Max. adhesion char.
PID
Fuzzy-PID
Asphalt (dry)
1.18
0.027
0.028
Asphalt (wet)
0.8
0.035
0.035
Concrete
1.1
0.028
0.029
Cobblestone (dry)
1
0.085
0.087
Cobblestone (wet)
0.34
0.26
0.33
Table 4.
Maximum derived slip ratios for PID and fuzzy-PID controllers.
It is also concluded that the mathematical derivation and its investigation of the brake system model are accurate and valid particularly because the examination and exploration of the output results are entirely identical to the analysis and investigation of each system’s variable as demonstrated in Section 3. Besides, the suggested feedback control signal which is based on wheel speed was able to deliver detailed and thorough information about the status of braking system which assists to update system’s variable effectively.
5. EPAS system
EPAS presents the continuing future of power-assisted steering technology for passenger vehicles and has already been started to appear in high-volume, lead-vehicle applications; more flexible than traditional hydraulic power-assisted steering (HPAS) system, the fact of EPAS is to supply steering assistance to the driver utilizing an electrically controlled electric motor. EPAS is a classic exemplary case of a smart actuator operating under feedback control. It can provide necessary assist torque in different car speeds and different driver torques [6]. It has been reported in [6] that among electric power-assisted steering (EPAS) system available for passenger cars, EPAS systems provide the best fuel consumption [7, 8, 9]. The plot shown in Figure 7 indicates that EPAS systems have the lowest fuel consumption in comparison to hydraulic power-assisted steering (HPAS) system with savings in excess of 3.0% in average and up to 3.5% in city driving [6].
Figure 7.
Typical EPAS fuel consumption saving.
According to the steering torque, automobile speed as well as road conditions, the system can provide the real-time assistant torque through assist motor to help driver steering and make steering easier and gentle, which guarantees that the driver has the best steering feel in the variety of operating conditions. At present, the design for the assist motor control have mainly two methods: the first one is motor current loop control based on classical control theory and the other one is the state-space model H∞ control or sliding mode control based on modern control theory [10]. Literature [11] using the motor current tracking control based on conventional PID achieved good results. But the system was not designed for different car speeds. Literature [12] established an EPAS mathematical model, and the simulation results showed that the strategy could achieve the desired characteristics, but the vehicle speed was not taken into account; the results had certain limitations [13, 14], using a sliding mode control that improved the system stability and anti-disturb capability but that increased the complexity of the control system, which set higher requirement of the computing power to the control ship. That is not beneficial to the promotion of products.
The aim of this study in EPAS is to control the electric motor to supply the appropriate assist torque to decrease the driver’s steering effort in various speeds. The EPAS control must ensure the generation of the desired assist torque, a stable system with a large amount of assistance. The most important issue is electric motor tracking precisely the target current. To develop the electric motor current tracking performance, particle swarm optimization (PSO) algorithm is applied as tuning mechanism for fractional-order PID (FOPID) controller.
6. System modeling
The EPAS includes a torque sensor, which senses the action of the driver along with the action of the automobile; an ECU, which performs calculations on assisting force based on signals from the torque sensor; a motor, which creates turning power based on the output from ECU; and a reduction gear, which increases the turning force from the motor and transfers it to the steering system and pinion and rack (Figure 8).
Figure 8.
EAPS dynamic model.
The parameters associated with the rack model are Mr (steering tie rod mass), Br(steering tie rod damping coefficient), Rs(radius of pinion steering), and Kr(tire spring rate) (Table 5).
The function of ECU is to collect the torque sensor and the vehicle speed signal, select a suitable motor target current by an assist characteristic curve, execute a control by comparing with the feedback actual current, and then drive the DC motor.
8. Fractional-order PID (FOPID) controllers
Fractional-order PID (FOPID) controller denoted by PIλDμ was proposed by Igor Podlubny [16] in 1997. It is an extension of conventional PID controller where λ and μ have fractional values. Figure 9 shows the block diagram of FOPID controller. The fractional-order PID (FOPID) controller is a generalization of the PID controller. The transfer function of the controller is written by the equation below:
Cs=Kp+KiSλ+KdSμE3
where Kp,Ki, and Kd are the proportional gain, integral gain, and derivative time constants, respectively, and λ and μ are fractional powers.
Figure 9.
Fractional-order PID controller.
where μ and λ are an arbitrary real numbers. Taking μ = 1 and λ = 1, a classical PID controller is obtained. Thus, FOPID controller generalizes the classical PID controller and expands it from point to plane as shown in Figure 10. This expansion provides us much more flexibility in designing PID controller and gives an opportunity to better adjust the dynamics of control system. This increases the robustness of the system and makes it more stable. However, with increase in parameters to be tuned, the optimization problem associated with the system becomes more difficult [17]. For achieving a certain performance, it is desired to develop a systematic algorithm for the FOPID optimization as shown in Figure 11.
Figure 10.
Control strategy.
Figure 11.
(a) Classical PID controller and (b) FOPID controller.
9. Simulation results
Figure 12 shows an open-loop response of system, as depicted in the figure below, that motor current cannot follow the step unit. The close-loop unit step response of EPAS system using classical PID controller and PSO-FOPID are shown in Figure 13.
Figure 12.
Unit step response of EPAS open-loop system.
Figure 13.
Unit step response of EPAS system using classical PID and optimal FOPID.
10. Controlled system in different speeds and different driver torques
Figure 14 shows three signals (i) motor current tracking of the look-up table, (ii) driving wheel input torque and (iii) the electric motor output current. The vehicle speed is 20km/h while the driver input torque is represented as a sine wave of 9 N.m amplitude.When the input torque of driving wheel Td is less than the threshold value Td0=1N.m, motor does not provide power, so the assist current would be zero, when Td is between Td0=1N.m and Tdmax=7N.m, motor current has a rising with Td, and it depends on the car speed. When Td is above Tdmax, the motor output is a constant torque.
Figure 14.
Driver torque and motor current in 20 km/h.
11. Conclusion
In this study, a design of fuzzy-PID controller for BBW system is presented. In addition to that, a design structure for BBW is proposed which helps to elaborate a principle of work of the suggested BBW system. The braking mechanism and operation of BBW system are grasped and realized by obtaining mathematical derivation of the brake system based on quarter car model. Two controller algorithms based on PID and fuzzy-PID controllers are then implemented to check the validity of mathematical derivation on the one side and to operate braking mechanism of BBW on the other side. The simulation result which is conducted on different road types and conditions shows that fuzzy-PID controller is a superior and outstanding controller as compared to PID controller, where the fuzzy-PID controller assists to reduce stopping vehicle time 60% and the most important thing is the ability of fuzzy-PID controller to improve the system performance by eliminating steady-state error to zero. Besides, the result analysis and investigation demonstrate that larger adhesion characteristics lead to produce larger brake force which in turn assists to reduce vehicle stopping time.
For EPAS system, FOPID (fractional-order PID) controller has been presented, and it was tuned to control the motor current. All simulations for the whole EPAS system are implemented by MATLAB/Simulink software showing a comparison of classical PID and optimal PID tracking performance. PSO algorithm has been implemented to find optimal values of FOPID parameters. From the simulation results, it fulfills the control objectives and achieves good assistant in different speeds.
\n',keywords:"x-by-wire, brake-by-wire, steer-by-wire",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/62507.pdf",chapterXML:"https://mts.intechopen.com/source/xml/62507.xml",downloadPdfUrl:"/chapter/pdf-download/62507",previewPdfUrl:"/chapter/pdf-preview/62507",totalDownloads:685,totalViews:270,totalCrossrefCites:1,dateSubmitted:"December 13th 2017",dateReviewed:"March 28th 2018",datePrePublished:"November 5th 2018",datePublished:"January 30th 2019",dateFinished:null,readingETA:"0",abstract:"The chapter emphasizes on the worked example of braking system and steering system for electric vehicle. The x-by-wire technology is investigated and validated comprehensively. Brake-by-wire is considered a new brake technology that uses electronic devices and control system instead of conventional brake components to carry out braking function based on wire-transmitted information. However, the physical parameters associated with braking function cause nonlinear characteristics and variations in the braking dynamics, which eventually degrade stability and performance of the system. Therefore, this study presents the design of fuzzy-PID controller for brake-by-wire (BBW) to overcome these undesired effects and also to derive optimal brake force that assists to perform braking operation under distinct road conditions and distinct road types. Electric power-assisted steering (EPAS) system is a new power steering technology for vehicles especially for electric vehicles (EV). It has been applied to displace conventional hydraulic power-assisted steering (HPAS) system due to space efficiency, environmental compatibility, and engine performance. An EPAS system is a driver-assisting feedback system designed to boost the driver input torque to a desired output torque causing the steering action to be undertaken at much lower steering efforts.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/62507",risUrl:"/chapter/ris/62507",signatures:"Ameer Sider and Mohd Khair Hassan",book:{id:"6767",title:"New Trends in Electrical Vehicle Powertrains",subtitle:null,fullTitle:"New Trends in Electrical Vehicle Powertrains",slug:"new-trends-in-electrical-vehicle-powertrains",publishedDate:"January 30th 2019",bookSignature:"Luis Romeral Martínez and Miguel Delgado Prieto",coverURL:"https://cdn.intechopen.com/books/images_new/6767.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"86501",title:"Dr.",name:"Luis",middleName:null,surname:"Romeral Martinez",slug:"luis-romeral-martinez",fullName:"Luis Romeral Martinez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"239688",title:"Associate Prof.",name:"Mohd Khair",middleName:null,surname:"Hassan",fullName:"Mohd Khair Hassan",slug:"mohd-khair-hassan",email:"khair@upm.edu.my",position:null,institution:{name:"Universiti Putra Malaysia",institutionURL:null,country:{name:"Malaysia"}}},{id:"249989",title:"Mr.",name:"Ameer",middleName:null,surname:"Seder",fullName:"Ameer Seder",slug:"ameer-seder",email:"asider@birzeit.edu",position:null,institution:{name:"Universiti Putra Malaysia",institutionURL:null,country:{name:"Malaysia"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. BBW design and principle of work",level:"1"},{id:"sec_3",title:"3. Control system design",level:"1"},{id:"sec_4",title:"4. Simulation result and analysis",level:"1"},{id:"sec_5",title:"5. EPAS system",level:"1"},{id:"sec_6",title:"6. System modeling",level:"1"},{id:"sec_7",title:"7. EPAS controller",level:"1"},{id:"sec_8",title:"8. Fractional-order PID (FOPID) controllers",level:"1"},{id:"sec_9",title:"9. Simulation results",level:"1"},{id:"sec_10",title:"10. Controlled system in different speeds and different driver torques",level:"1"},{id:"sec_11",title:"11. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Mingfei Y. Brake-by-wire System design and simulation. In: International Conference of Computer Science and Electronics Engineering (ICCSEE), 2012. Hangzhou; 2012. pp. 248-251'},{id:"B2",body:'Xiang W, Richardson PC, Zhao C, Mohammad S. Automobile brake-by-wire control system design and analysis. 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IEEE Transactions on Industrial Electronics. 2004;51(2):290-298'},{id:"B13",body:'Marouf A, et al. Control of electric power assisted steering system using sliding mode control. In: Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on. Nagoya, Japan. 2011'},{id:"B14",body:'Marouf A et al. A new control strategy of an electric-power-assisted steering system. IEEE Transactions on Vehicular Technology. 2012;61(8):3574-3589'},{id:"B15",body:'Hassan M et al. Optimal design of electric power assisted steering system (EPAS) using GA-PID method. Procedia Engineering. 2012;41:614-621'},{id:"B16",body:'Dzielinski A, Sierociuk D. Simulation and experimental tools for fractional order control education. IFAC Proceedings Volumes. 2008;41(2):11654-11659'},{id:"B17",body:'Padhee S et al. A novel evolutionary tuning method for fractional order PID controller. 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\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
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Does your institution already have a budget for covering Open Access publication costs?
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Does your grant list Open Access publication fees as legitimate direct/indirect costs?
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Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at oapf@intechopen.com
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