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This softcover book summarizes Lyapunov design techniques for nonlinear systems and raises important issues concerning large-signal robustness and performance. The authors have been the first to address some of these issues, and they report their findings in this text. The researcher who wishes to enter the field of robust nonlinear control could use this book as a source of new research topics. For those already active in the field, the book may serve as a reference to a recent body of significant work. Finally, the design engineer faced with a nonlinear control problem will benefit from the techniques presented here.
The underlying theory on which much modern robust and nonlinear control is based can be difficult to grasp. This volume is a collection of lecture notes presented by experts in advanced control engineering. The book is designed to provide a better grounding in the theory underlying several important areas of control. It is hoped the book will help the reader to apply otherwise abstruse ideas of nonlinear control in a variety of real systems.
Nonlinear Control Design presents a self-contained introduction to nonlinear feedback control design for continuous time, finite-dimensional uncertain systems. It deals with nonlinear systems affected by uncertainties such as unknown constant parameters, time-varying disturbances, and uncertain nonlinearities. Both state feedback and output feedback are addressed. Differential geometric techniques are used to identify classes of nonlinear systems considered and to design feedback algorithms. Adaptive versions of these controls are developed in the presence of unknown parameters while robust versions are designed in the presence of time-varying disturbances. These control algorithms are applied to significant physical control problems from electric motor drives, robotics, aerospace, power systems and are illustrated through worked examples. The text is illustrated throughout with over 100 exercises, more than 75 worked examples and 12 physical examples.
This book provides a unified collection of important, recent results for the design of robust controllers for uncertain systems. Most of the results presented are based on H? control theory, or its stochastic counterpart, risk sensitive control theory.Central to the philosophy of the book is the notion of an uncertain system. Uncertain systems are considered using several different uncertainty modeling schemes. These include norm bounded uncertainty, integral quadratic constraint (IQC) uncertainty and a number of stochastic uncertainty descriptions. In particular, the authors examine stochastic uncertain systems in which the uncertainty is outlined by a stochastic version of the IQC uncertainty description.For each class of uncertain systems covered in the book, corresponding robust control problems are defined and solutions discussed.
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.).
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 book is intended as a graduate text as weil as a reference to scientists and engineers involved in the analysis and design of feedback systems. The first version of this book was written in 1983, while I was teach ing at the Department of Systems Science and Mathematics at Washington University in St. Louis. This new edition integrates my subsequent teaching experience gained at the University of Illinois in Urbana-Champaign in 1987, at the Carl-Cranz Gesellschaft in Oberpfaffenhofen in 1987, at the University of California in Berkeley in 1988. In addition to a major rearrangement of the last two Chapters of the first version, this new edition incorporates two additional Chapters at a more elementary level and an exposition of some relevant research findings which have occurred since 1985.
Shows readers how to exploit the capabilities of the MATLAB® Robust Control and Control Systems Toolboxes to the fullest using practical robust control examples.