Systems & Control

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Systems and Control covers modeling, analysis, and control of dynamical systems. Introducing students to the basics of dynamical system theory and supplying them with the tools necessary for control system design, it emphasizes design and demonstrates how dynamical system theory fits into practical applications. Classical methods and the techniques of postmodern control engineering are presented in a unified fashion, demonstrating how the current tools of a control engineer can supplement more classical tools. Broad in scope, Systems and Control shows the multidisciplinary role of dynamics and control; presents neural networks, fuzzy systems, and genetic algorithms; and provides a self-contained introduction to chaotic systems. The text employs Lyapunov s stability theory as a unifying medium for different types of dynamical systems, using it--with its variants--to analyze dynamical system models. Specifically, optimal, fuzzy, sliding mode, and chaotic controllers are all constructed with the aid of the Lyapunov method and its extensions. In addition, a class of neural networks is also analyzed using Lyapunov s method. Key features Aquaints students with the basics of dynamical system theory and equips them with the tools necessary for control system design. Covers both classical methods and the techniques of post-modern control engineering. Accompanied by Instructor's Manual.

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Stanislaw H. Zak Professor of Electrical and Computer Engineering at Purdue. He has worked in various areas of control, optimization, and neural networks. He is coauthor of Selected Methods of Analysis of Linear Dynamical Systems (1984, in Polish) and An Introduction to Optimization, 2/e (2001). Dr. Zak has also contributed to the Comprehensive Dictionary of Electrical Engineering (1999) and is a past associate editor of Dynamics and Control and the IEEE Transactions on Neural Networks. Table of Contents Each chapter ends with Notes and ExercisesPreface Dynamical Systems and Modeling Analysis of Modeling Equations Linear Systems Stability Optimal Control Sliding Modes Vector Field Methods Fuzzy Systems Neural Networks Genetic and Evolutionary Algorithms