Books in the Communications and Control Engineering series

Input-to-State Stability presents the dominating stability paradigm in nonlinear control theory that revolutionized our view on stabilization of nonlinear systems, design of robust nonlinear observers, and stability of nonlinear interconnected control systems.The applications of input-to-state stability (ISS) are manifold and include mechatronics, aerospace engineering, and systems biology. Although the book concentrates on the ISS theory of finite-dimensional systems, it emphasizes the importance of a more general view of infinite-dimensional ISS theory. This permits the analysis of more general system classes and provides new perspectives on and a better understanding of the classical ISS theory for ordinary differential equations (ODEs).Features of the book include: ¿ a comprehensive overview of the theoretical basis of ISS; ¿ a description of the central applications of ISS in nonlinear control theory; ¿ a detailed discussion of the role ofsmall-gain methods in the stability of nonlinear networks; and ¿ an in-depth comparison of ISS for finite- and infinite-dimensional systems.The book also provides a short overview of the ISS theory for other systems classes (partial differential equations, hybrid, impulsive, and time-delay systems) and surveys the available results for the important stability properties that are related to ISS.The reader should have a basic knowledge of analysis, Lebesgue integration theory, linear algebra, and the theory of ODEs but requires no prior knowledge of dynamical systems or stability theory. The author introduces all the necessary ideas within the book.Input-to-State Stability will interest researchers and graduate students studying nonlinear control from either a mathematical or engineering background. It is intended for active readers and contains numerous exercises of varying difficulty, which are integral to the text, complementing and widening the material developed in the monograph.

This monograph provides a state-of-the-art treatment of learning and robust control in quantum technology. It presents a systematic investigation of control design and algorithm realisation for several classes of quantum systems using control-theoretic tools and machine-learning methods. The approaches rely heavily on examples and the authors cover:sliding mode control of quantum systems;control and classification of inhomogeneous quantum ensembles using sampling-based learning control;robust and optimal control design using machine-learning methods;robust stability of quantum systems; and H¿ and fault-tolerant control of quantum systems. Both theoretical algorithm design and potential practical applications are considered. Methods for enhancing robustness of performance are developed in the context of quantum state preparation, quantum gate construction, and ultrafast control of molecules.Researchers and graduates studying systems and control theory, quantum control, and quantum engineering, especially from backgrounds in electrical engineering, applied mathematics and quantum information will find Learning and Robust Control in Quantum Technology to be a valuable reference for the investigation of learning and robust control of quantum systems. The material contained in this book will also interest chemists and physicists working on chemical physics, quantum optics, and quantum information technology.

This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors' reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods.The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science.This is an open access book.

Control of Wave and Beam PDEs is a concise, self-contained introduction to Riesz bases in Hilbert space and their applications to control systems described by partial differential equations (PDEs).

The book is the first book on complex matrix equations including the conjugate of unknown matrices. In addition, the new concept of conjugate product for complex polynomial matrices is also proposed in order to establish a unified approach for solving a type of complex matrix equation.

In this book, rather than emphasize differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates as high as they are, it is more appropriate to emphasise connections and similarities.

Offering readers a wealth of cutting-edge, Riccati-based design techniques for various forms of control, this self-contained text stress-tests the reliability of the methods outlined with rigorous stability analyses and detailed control design algorithms.

Thoroughly revised and updated, this second edition of Adaptive Control covers new developments in the field, including multi-model adaptive control with switching, direct and indirect adaptive regulation, and adaptive feedforward disturbance compensation.

This volume presents recent and notable progress in the mathematical theory of stabilization of Newtonian fluid flows. It avoids the tedious technical details often seen in mathematical treatments of the subject and will thus appeal to a wide range of readers.

An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts. Part I deals with the mathematical preliminaries: numerical linear algebra;

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.

Focuses on a class of nonsmooth hybrid dynamical systems, namely finite-dimensional mechanical systems subject to unilateral constraints. This book contains overview of the main problems in mathematics, mechanics, stability and control. It discusses such topics as: shock dynamics; multiple impacts; feedback control; and, Moreau's sweeping process.

This book reports on recent achievements in stability and feedback stabilization of infinite systems. Various control methods such as sensor feedback control and dynamic boundary control are applied to stabilize the equations. Many new theorems and methods are included in the book.

Comprehensive treatment of approximation methods for filters and controllers. Balanced truncation, Hankel norm reduction, multiplicative reduction, weighted methods and coprime factorization methods are all discussed.

This is a unified collection of important recent results for the design of robust controllers for uncertain systems, primarily based on H8 control theory or its stochastic counterpart, risk sensitive control theory. Two practical applications are used to illustrate the methods throughout.

This second edition of Dissipative Systems Analysis and Control has been substantially reorganized to accommodate new material and enhance its pedagogical features. Throughout, emphasis is placed on the use of the dissipative properties of a system for the design of stable feedback control laws.

Unique in its systematic approach to stochastic systems, this book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces.

This eagerly awaited follow-up to Nonlinear Control Systems incorporates recent advances in the design of feedback laws, for the purpose of globally stabilizing nonlinear systems via state or output feedback. The author is one of the most prominent researchers in the field.

New results, fresh ideas and new applications in automotive and flight control systems are presented in this second edition of Robust Control.

The purpose of this book is to present a self-contained description of the fun damentals of the theory of nonlinear control systems, with special emphasis on the differential geometric approach.

The purpose of this book is to present a self-contained description of the fun damentals of the theory of nonlinear control systems, with special emphasis on the differential geometric approach.