MATLAB Simulation of System Identification and Adaptive Control (Revised Edition)

This book systematically introduces fundamental theories and methods of system identification and adaptive control from the perspective of MATLAB simulation. The content is organized into six chapters: Introduction, System Identification, Model Reference Adaptive Control, Self-tuning Control (including Generalized Predictive Control), Conventional Control Strategy-based Self-tuning Control, and Nonlinear System Identification and Control (including Neural Networks and Fuzzy Control). The Introduction chapter establishes basic concepts and significance of system identification and adaptive control, providing essential theoretical foundations for subsequent implementations. The System Identification chapter delves into various identification methods including parameter identification and non-parametric identification, accompanied by multiple simulation examples with detailed MATLAB code demonstrations showing practical implementation using System Identification Toolbox functions like arx(), pem(), and n4sid(). The Model Reference Adaptive Control chapter explains core principles through simulation examples that demonstrate real-time parameter adjustment algorithms using Lyapunov stability theory implementations. The Self-tuning Control chapter explores methods including Generalized Predictive Control (GPC) with MATLAB implementations showcasing recursive least squares algorithms and predictive control optimization techniques. The Conventional Control Strategy-based Self-tuning Control chapter presents methods integrating traditional PID controllers with adaptive mechanisms, featuring simulation examples that illustrate automatic tuning procedures using cost function minimization approaches. The Nonlinear System Identification and Control chapter covers neural network implementations (using Neural Network Toolbox functions like feedforwardnet()) and fuzzy logic controllers (using Fuzzy Logic Toolbox), with examples demonstrating nonlinear system modeling and control design. Throughout the book, extensive simulation examples include complete MATLAB code, numerical results, and analytical discussions highlighting key implementation aspects such as algorithm convergence, parameter sensitivity, and performance evaluation metrics. All code examples emphasize practical considerations including computational efficiency, real-time implementation constraints, and performance validation techniques.