Non-Fiction Books:

Modern Anti-windup Synthesis

Control Augmentation for Actuator Saturation
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Description

This book provides a wide variety of state-space--based numerical algorithms for the synthesis of feedback algorithms for linear systems with input saturation. Specifically, it addresses and solves the anti-windup problem, presenting the objectives and terminology of the problem, the mathematical tools behind anti-windup algorithms, and more than twenty algorithms for anti-windup synthesis, illustrated with examples. Luca Zaccarian and Andrew Teel's modern method--combining a state-space approach with algorithms generated by solving linear matrix inequalities--treats MIMO and SISO systems with equal ease. The book, aimed at control engineers as well as graduate students, ranges from very simple anti-windup construction to sophisticated anti-windup algorithms for nonlinear systems. * Describes the fundamental objectives and principles behind anti-windup synthesis for control systems with actuator saturation * Takes a modern, state-space approach to synthesis that applies to both SISO and MIMO systems * Presents algorithms as linear matrix inequalities that can be readily solved with widely available software * Explains mathematical concepts that motivate synthesis algorithms * Uses nonlinear performance curves to quantify performance relative to disturbances of varying magnitudes * Includes anti-windup algorithms for a class of Euler-Lagrange nonlinear systems * Traces the history of anti-windup research through an extensive annotated bibliography

Author Biography:

Luca Zaccarian is associate professor of control engineering at the University of Rome, Tor Vergata. Andrew R. Teel is a professor in the Electrical and Computer Engineering Department at the University of California, Santa Barbara.
Release date Australia
July 31st, 2011
Audiences
  • Professional & Vocational
  • Tertiary Education (US: College)
Illustrations
6 halftones. 135 line illus. 5 tables.
Pages
304
Dimensions
152x229x25
ISBN-13
9780691147321
Product ID
10363515

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