Model-Reference Adaptive Control

Abstract

This chapter presents the fundamental theory of model-reference adaptive control. Various types of uncertainty are defined. The composition of a model-reference adaptive control system is presented. Adaptive control theory for first-order single-input single-output (SISO) systems, second-order SISO systems, and multiple-input multiple-output (MIMO) systems is presented. Both direct and indirect adaptive control methods are discussed. The direct adaptive control methods adjust the control gains online directly, whereas the indirect adaptive control methods estimate unknown system parameters for use in the update of the control gains. Asymptotic tracking is the fundamental property of model-reference adaptive control which guarantees that the tracking error tends to zero in the limit. On the other hand, adaptive parameters are only bounded in the model-reference adaptive control setting.