Bøger i Communications and Control Engineering serien

Control Theory for Linear Systems deals with the mathematical theory of feedback control of linear systems. It treats a wide range of control synthesis problems for linear state space systems with inputs and outputs. The book provides a treatment of these problems using state space methods, often with a geometric flavour. Its subject matter ranges from controllability and observability, stabilization, disturbance decoupling, and tracking and regulation, to linear quadratic regulation, H2 and H-infinity control, and robust stabilization. Each chapter of the book contains a series of exercises, intended to increase the reader's understanding of the material. Often, these exercises generalize and extend the material treated in the regular text.

This book focuses on the observability of hybrid systems. It enables the reader to determine whether and how a hybrid system¿s state can be reconstructed from sometimes necessarily partial information. By explaining how available measurements can be used to deduce past and future behaviours of a system, the authors extend this study of observability to embrace the properties of diagnosability and predictability.H-systems shows how continuous and discrete dynamics and their interaction affect the observability of this general class of hybrid systems and demonstrates that hybrid characteristics are not simply generalizations of well-known aspects of traditional dynamics. The authors identify conditions for state reconstruction, prediction and diagnosis of the occurrence of possibly faulty states. The formal approach to proving those properties for hybrid systems is accompanied by simple illustrative examples. For readers who are interested in the use of state estimation for controller design, the book also provides design methods for hybrid state observers and covers their application in some industrial cases.The book¿s tutorial approach to the various forms of observability of hybrid systems helps to make H-systems of interest to academic researchers and graduate students working in control and to practitioners using control in an industrial environment.

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.

Estimation and Inference in Discrete Event Systems chooses a popular model for emerging automation systems-finite automata under partial observation-and focuses on a comprehensive study of the key problems of state estimation and event inference.

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).

Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc.Features of the text include:¿ a general functional optimization framework;¿ thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems;¿ comparison of classical and neural-network based methods of approximate solution;¿ bounds to the errors of approximate solutions;¿ solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several;¿ applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and¿ numerous, numerically detailed examples.The authors¿ diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.

Numerical examples in the book use SLRA and IDENT packages. Material available for download: Software implementation of algorithms in the book, demonstrations and case studies. Problems and solutions. Lecture slides for a course based on the book.

MATLAB (R) codes for worked examples for optimal and sub-optimal control

NMPC software both in MATLAB (R) (for smaller academic examples) and C++ (for larger examples) will be provided.

Both measurement-based feedback control (i.e., feedback control by a classical system involving a continuous-time measurement process) and coherent feedback control (i.e., feedback control by another quantum system without the intervention of any measurements in the feedback loop) are treated.

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.

Analysis and Design of Networked Control Systems

Passivity-Based Control and Estimation in Networked Robotics

This book shows how the use of S-variables (SVs) in enhancing the range of problems that can be addressed with the already-versatile linear matrix inequality (LMI) approach to control can, in many cases, be put on a more unified, methodical footing.

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.