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.

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