This book offers comprehensive coverage of identification and adaptive control while familiarizing graduate students and practicing engineers with computational software tools such as MATLAB and SIMULINK and describing the underlying theoretical concepts. Identification is the process of mathematically modeling a system based on measurement data that may be limited or uncertain. Adaptive control is the means whereby a system that is poorly modeled is controlled adequately. Therefore the topical coverage is divided into two parts: Part I describes fundamental topics of system identification independent of adaptive control and discusses nonparametric and parameteric estimation methods while emphasizing least squares techniques instrumental variables and prediction error methods. Part II describes various methods of adaptive control in which the materials discussed in Part I are essential for control purposes, including model reference, adaptive control and self-tuning regulators.

Learning process fully covered which allows students to simulate own system identification and adaptive control systems

Part I: Systems Indentification.- Introduction.- Signal and System Models.- Nonparametric Identification Methods.- Least Squares Theory.- Input Signals, Persistent Excitation, and Model Parametrizations.- System Identification by Least Squares and Generalized Least Squares.- Recursive Estimation.- Instrumental Variable Method.- Prediction Error Method.- Model Validation.- Part II: Adaptive Control.- System Identification and Adaptive Control.- Model Reference Adaptive Control.- Self Tuning Regulator.- Adaptive Pole Placement.- Adaptive Observer.- Robust Adaptive Control.