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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques (Adaptive and Learning Systems for Signal Processing, Communications and Control)by Jeffrey T. Spooner
Synopses & Reviews
A powerful, yet easy-to-use design methodology for the control of nonlinear dynamic systems
A key issue in the design of control systems is proving that the resulting closed-loop system is stable, especially in cases of high consequence applications, where process variations or failure could result in unacceptable risk. Adaptive control techniques provide a proven methodology for designing stable controllers for systems that may possess a large amount of uncertainty. At the same time, the benefits of neural networks and fuzzy systems are generating much excitement-- and impressive innovations-- in almost every engineering discipline.
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques brings together these two different but equally useful approaches to the control of nonlinear systems in order to provide students and practitioners with the background necessary to understand and contribute to this emerging field.
The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with "intelligent control" approaches. The authors show how these methodologies may be applied to many real-world systems including motor control, aircraft control, industrial automation, and many other challenging nonlinear systems. They provide explicit guidelines to make the design and application of the various techniques a practical and painless process.
Design techniques are presented for nonlinear multi-input multi-output (MIMO) systems in state-feedback, output-feedback, continuous or discrete-time, or even decentralized form. To help students and practitioners new to the field grasp and sustain mastery of the material, the book features:
* Background material on fuzzy systems and neural networks
* Step-by-step controller design
* Numerous examples
* Case studies using "real world" applications
* Homework problems and design projects
Book News Annotation:
Two useful approaches to the control of nonlinear systems are brought together for students and practitioners in need of background information. The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility associated with intelligent control approaches. Examples demonstrate how the methodology can be applied to real-world systems such as motor control, aircraft control, industrial automation, and other nonlinear systems. Guidelines are provided for design and application of techniques, and design techniques are presented for nonlinear multi- input multi-output systems. Homework problems and design projects are included. Spooner is a senior member of the technical staff at Sandia National Laboratories.
Annotation c. Book News, Inc., Portland, OR (booknews.com)
Homework problems and design projects
Thema dieses Buches ist die Anwendung neuronaler Netze und Fuzzy-Logic-Methoden zur Identifikation und Steuerung nichtlinear-dynamischer Systeme. Dabei werden fortgeschrittene Konzepte der herkömmlichen Steuerungstheorie mit den intuitiven Eigenschaften intelligenter Systeme kombiniert, um praxisrelevante Steuerungsaufgaben zu lösen. Die Autoren bieten viel Hintergrundmaterial; ausgearbeitete Beispiele und Übungsaufgaben helfen Studenten und Praktikern beim Vertiefen des Stoffes. Lösungen zu den Aufgaben sowie MATLAB-Codebeispiele sind ebenfalls enthalten.
About the Author
JEFFREY T. SPOONER is a senior member of the technical staff at Sandia National Laboratories, Albuquerque, New Mexico.
MANFREDI MAGGIORE is an assistant professor in the Department of Electrical and Computer Engineering at the University of Toronto, Canada.
RAUL ORDO?EZ is an assistant professor in the Department of Electrical and Computer Engineering at the University of Dayton, Ohio.
KEVIN M. PASSINO is a professor in the Department of Electrical Engineering at The Ohio State University.
Table of Contents
PART I: FOUNDATIONS.
Neural Networks and Fuzzy Systems.
Optimization for Training Approximators.
PART II: STATE-FEEDBACK CONTROL.
Control of Nonlinear Systems.
Direct Adaptive Control.
Indirect Adaptive Control.
Implementations and Comparative Studies.
PART III:OUTPUT-FEEDBACK CONTROL.
Adaptive Output Feedback Control.
PART IV: EXTENSIONS.
Perspectives on Intelligent Adaptive Systems.
For Further Study.
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