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Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility - at the price of complexity and di?cult tuning. The progressive evolution has been mainly ledby speci?capplicationsandshorttermtargets,withthe consequencethat automotive control is to a very large extent more heuristic than systematic. Product requirements are still increasing and new challenges are coming from potentially huge markets like India and China, and against this ba- ground there is wide consensus both in the industry and academia that the current state is not satisfactory. Model-based control could be an approach to improve performance while reducing development and tuning times and possibly costs. Model predictive control is a kind of model-based control design approach which has experienced a growing success since the middle of the 1980s for slow  complex plants, in particular of the chemical and process industry. In the last decades, severaldevelopments haveallowedusing these methods also for fast systemsandthis hassupporteda growinginterestinitsusealsofor automotive applications, with several promising results reported. Still there is no consensus on whether model predictive control with its high requi- ments on model quality and on computational power is a sensible choice for automotive control.

Engineering. --- Control. --- Automotive Engineering. --- Ingénierie --- Automatic control --- Control theory

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Control of Complex Systems: Structural Constraints and Uncertainty focuses on control design under information structure constraints, with a particular emphasis on large-scale systems. The complexity of such systems poses serious computational challenges and severely restricts the types of feedback laws that can be used in practice. This book systematically addresses the main issues, and provides a number of applications that illustrate potential design methods, most which use Linear Matrix Inequalities (LMIs), which have become a popular design tool over the past two decades. Authors Aleksandar I. Zecevic and Dragoslav D. Siljak use their years of experience in the control field to also: Address the issues of large-scale systems as they relate to robust control and linear matrix inequalities Discuss a new approach to applying standard LMI techniques to large-scale systems, combining graphic-theoretic decomposition techniques with appropriate low-rank numerical approximations and dramatically reducing the computational effort Providing numerous examples and a wide variety of applications, ranging from electric power systems and nonlinear circuits to mechanical problems and dynamic Boolean networks Control of Complex Systems: Structural Constraints and Uncertainty will appeal to practicing engineers, researchers and students working in control design and other related areas. 

Engineering. --- Control, Robotics, Mechatronics. --- Vibration, Dynamical Systems, Control. --- Systems Theory, Control. --- Systems theory. --- Vibration. --- Ingénierie --- Vibration --- Control theory --- Large scale systems

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Mathematical control theory of applied partial differential equations is built on linear andnonlinearfunctionalanalysisand manyexistencetheoremsin controlt- ory result from applications of theorems in functional analysis. This makes control theoryinaccessibleto studentswhodo nothave a backgroundin functionalanalysis. Many advanced control theory books on in?nite-dimensionalsystems were wr- ten, using functional analysis and semigroup theory, and control theory was p- sented in an abstract setting. This motivates me to write this text for control theory classes in the way to present control theory by concrete examples and try to m- imize the use of functional analysis. Functional analysis is not assumed and any analysis included here is elementary, using calculus such as integration by parts. The material presented in this text is just a simpli?cation of the material from the existing advanced control books. Thus this text is accessible to senior undergra- ate studentsand?rst-yeargraduatestudentsin appliedmathematics,who havetaken linear algebra and ordinary and partial differential equations. Elementary functional analysis is presented in Chapter 2. This material is - quired to present the control theory of partial differential equations. Since many control conceptsand theories for partial differentialequations are transplanted from ?nite-dimensionalcontrol systems, a brief introduction to feedback control of these systemsispresentedinChapter3.Thetopicscoveredinthischapterincludecontr- lability, observability,stabilizability, pole placement, and quadratic optimal control.

Mathematics. --- Partial Differential Equations. --- Applications of Mathematics. --- Control. --- Differential equations, partial. --- Mathématiques --- Control theory --- Wave equation --- Control theory. --- Feedback control systems. --- Wave equation.

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Biological control is among the most promising methods for control of pests (including vectors), diseases and weeds. In this book ecological and societal aspects are for the first time treated together. In an ecological approach the aim is to evaluate the significance of certain biological properties like biodiversity and natural habitats. Also, it is important to see biological control from an organic (or ecological) farming point of view. In a societal approach terms like ˜consumer's attitude', ˜risk perception', ˜learning and education' and ˜value triangle' are recognised as significant for biological production and human welfare.

Biological control --- ecology --- Biological control agents --- natural enemies --- socioeconomic environment --- Innovation adoption --- Economic sociology

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It is well known that computer algebra systems have revolutionized teaching and the learning processes in mathematics, science, and - gineering, allowing students to computationally investigate complicated problems to ?nd exact or approximate analytic solutions, numeric so- tions, and illustrative two- and three-dimensional graphics. Since the 1960s there has existed individual packages for solving s- ci?c analytic, numerical, graphical and other problems. The need to solve all those problems with the aid of a single system, has led to the idea of construction of a modern general purpose computer algebra s- tem. The ?rst two papers describing analytic calculations realized on a computer were published in 1953 [7]. In the early 1970s, systems of - alytic computations (SAC),or computer algebra systems (CAS), began to appear. Computer algebra systems are computational interactive programs that facilitate symbolic mathematics and can handle other type of pr- lems. The ?rst popular systems were Reduce, Derive, and Macsyma, which are still commercially available. Macsyma was one of the ?rst and most mature systems. It was developed at the Massachusetts - stitute of Technology (MIT), but practically its evolution has stopped since thesummer of 1999. Afree software version of Macsyma, Maxima, is actively being maintained. To the present day, there have been developed more than a hundred computer algebra systems [7], [18]. Among these we can ?nd Axiom, Derive, Maxima, Maple, Mathematica, Matlab, MuMATH, MuPAD, Reduce, etc. All these systems can be subdivided into specialized and general-purpose computer algebra systems ([7], [18], [2]).

Mathematical control systems --- Computer science --- Computer. Automation --- informatica --- wiskunde --- informaticaonderzoek