Bücher der Reihe Communications and Control Engineering

The essence of this work is the control of electromechanical systems, such as manipulators, electric machines, and power converters. Amongst other topics, the authors cover: Euler-Lagrange Systems, Mechanical Systems, Generalised AC Motors, Induction Motor Control, Robots with AC Drives, and Perspectives and Open Problems.

This book provides readers with a systematic and unified framework for identification and adaptive control of Takagi-Sugeno (T-S) fuzzy systems. The book embodies a systematic study of fuzzy system identification and control problems, using T-S fuzzy system tools for both function approximation and feedback control of nonlinear systems.

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.

This book details the theory, algorithms, and applications of structured low-rank approximation, and presents efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel and Sylvester structured problems and more.

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.

Passivity-Based Control and Estimation in Networked Robotics

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.

The ZODIAC

Constructive Nonlinear Control presents a broad repertoire of constructive nonlinear designs not available in other works by widening the class of systems and design tools. Recursive Lyapunov designs for feedback, feedforward and interlaced structures result in feedback systems with optimality properties and stability margins.

This book deals with the issue of fundamental limitations in filtering and control system design. Although the original results were restricted to open-loop stable systems, they have been subsequently extended to open-loop unstable systems and systems having nonminimum phase zeros.

Control Theory for Linear Systems deals with the mathematical theory of feedback control of linear systems. 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.

Introducing passivity as a design tool for multi-agent systems, this book provides a unified framework for multi-agent coordination problems and discusses numerous related factors such as formation control, attitude coordination, and synchronization.

This book presents a collection of new methods of control for complex dynamical systems, some of them developed by the authors in the past 15 years. Mechanical and electromechanical systems described by nonlinear Lagrange's equations are considered.

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 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.

This is a self-contained introduction to algebraic control for nonlinear systems suitable for researchers and graduate students. It is the first book dealing with the linear-algebraic approach to nonlinear control systems in such a detailed and extensive fashion.

This text covers recent results in the analysis, identification and control of systems described by Volterra models. This work is suitable as a text for a graduate control course or as a reference for research and/or practice. Examples are drawn from chemical, biological and electrical engineering.

The extraordinary development of digital computers (microprocessors, microcontrollers) and their extensive use in control systems in all fields of applications has brought about important changes in the design of control systems.

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;

This book presents a thorough investigation of stability effects on three broad classes of switching mechanism: arbitrary switching, constrained switching, and designed switching. Includes many motivating and illustrative examples.

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.

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.

Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems.

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.

This accessible book pioneers feedback concepts for control mixing. It reviews research results appearing over the last decade, and contains control designs for stabilization of channel, pipe and bluff body flows, as well as control designs for the opposite problem of mixing enhancement.

Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems.

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.