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ABOUT THE BOOK

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.

TABLE OF CONTENT

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