Advanced Adaptive Control

For three decades, adaptive control has been an important area for basic theoretical research into the autonomous control of a priori unknown dynamical processes. Much of this study has been devoted to studies and applications associated with linear time invariant processes subject to Gaussian disturbances or mismodelling errors.
Advanced adaptive control extends this theory to encompass temporal and spatial parametric variations (through operating point changes), nonlinear dynamics, and non-Gaussian disturbances/distributions. The prohibitive complexity that this would bring to conventional mathematical methods (such as nonlinear time series analysis, frequency domain methods) has lead to the evolution of intelligent control methods based on ideas and techniques from such areas as neurophysiology, cognitive sciences, operational research, approximation theory and control theory which offer new research opportunities in adaptive control.
This book addresses some of the major issues and methods in advanced adaptive control via some recent results of the authors. Coverage includes: the utilization of adaptive or self-organizing artificial neural networks, fuzzy logic and rule based methods to solve nonlinear adaptive control problems for unknown plants; extending the current self-tuning control strategies to a priori unknown linear systems subject to general external disturbances; and the construction of adaptive control schemes for singular systems, for which the system is expressed as a combination of dynamic and algebraic equations.