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.
This text aims to bridge the gap between basic classical and modern control principles and the more advanced optimal control theories, dealing with multivariable control system design and computer-aided control system design tools (CACSD).
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