Stable Adaptive Control and Estimation for Nonlinear Systems

Neural and Fuzzy Approximator Techniques

Jeffrey T. Spooner, Manfredi Maggiore, Raul Ordoñez, Kevin M. Passino

546 pages, parution le 26/06/2002

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Résumé

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

Contents

I: FOUNDATIONS.
Mathematical Foundations.
Neural Networks and Fuzzy Systems.
Optimization for Training Approximators.
Function Approximation.
II: STATE-FEEDBACK CONTROL.
Control of Nonlinear Systems.
Direct Adaptive Control.
Indirect Adaptive Control.
Implementations and Comparative Studies.
III:OUTPUT-FEEDBACK CONTROL.
Output-Feedback Control.
Adaaptive Output Feedback Control.
Applications.
IV: EXTENSIONS.
Discrete-Time Systems.
Decentralized Systems.
Perspectives on Intelligent Adaptive Systems.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Jeffrey T. Spooner, Manfredi Maggiore, Raul Ordoñez, Kevin M. Passino
Parution	26/06/2002
Nb. de pages	546
Format	16 x 24
Couverture	Relié
Poids	924g
Intérieur	Noir et Blanc
EAN13	9780471415466
ISBN13	978-0-471-41546-6