1st Edition

Control Systems
Classical, Modern, and AI-Based Approaches




ISBN 9780815346302
Published July 24, 2019 by CRC Press
634 Pages 438 B/W Illustrations

USD $179.95

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Book Description

Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available.

Table of Contents

Section I: Linear and Nonlinear Control

1. Linear Systems and Control

2. Nonlinear Systems

3. Nonlinear Stability Analysis

4. Nonlinear Control Design

Section II: Optimal and H-Infinity Control

5. Optimization-Extremization of Cost Function

6. Optimal Control

7. Model Predictive Control

8. Robust Control

Section III: Digital and Adaptive Control

9. Discrete Time Control Systems

10. Design of Discrete Time Control Systems

11. Adaptive Control

12. Computer-Controlled Systems

Section IV: AI-Based Control

13. Introduction to AI-Based Control

14. ANN-Based Control Systems

15. Fuzzy Control Systems

16. Nature Inspired Optimization for Controller Design

Section V: System Theory and Control Related Topics

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Author(s)

Biography

Jitendra R. Raol, PhD, is Emeritus Professor at the M. S. Ramaiah Institute of Technology in Bangalore, India. He previously served at the National Aerospace Laboratories (NAL) as Scientist-G and Head of the Flight Mechanics and Control Division (FMCD). He is a fellow of the IEE (UK), a senior member of the IEEE (US), a life-fellow of the Aeronautical Society of India, and a life member of the System Society of India. He has guided nearly a dozen doctoral research scholars and is a reviewer of many international journals.

Ramakalyan Ayyagari, PhD, is with the Department of Instrumentation and Control Engineering at the National Institute of Technology—a deemed University, Tiruchirappalli, India. He earned a master’s at Andhra University, India, and a PhD at the Indian Institute of Technology, Delhi. Dr. Ayyagari’s areas of specialty include cyber physical systems, network flow control, modeling and control of big data systems, and path planning.

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