In parallel with developments in adaptive nonlinear control, there has been a tremendous amount of activity in neural control and adaptive fuzzy approaches. In these studies, neural networks or fuzzy approximators are used to approximate unknown nonlinearities. The input/output response of the approximator is modified by adjusting the values of certain parameters, usually referred to as weights. From a mathematical control perspective, neural networks and fuzzy approximators represent just two classes of function approximators. Polynomials, splines, radial basis functions, and wavelets are examples of other function approximators that can be used and have been used in a similar setting. We refer to such approximation models with adaptivity features as adaptive approximators, and control methodologies that are based on them as adaptive approximation based control. Adaptive approximation based control encompasses a variety of methods that appear in the literature: intelligent control, neural control, adaptive fuzzy control, memory-based control, knowledge-based control, adaptive nonlinear control, and adaptive linear control. |
During the last few years there have been significant developments in the control of highly uncertain, nonlinear dynamical systems. For systems with parametric uncertainty, adaptive nonlinear control has evolved as a powerful methodology leading to global stability and tracking results for a class of nonlinear systems. Advances in geometric nonlinear control theory, in conjunction with the development and refinement of new techniques, such as the backstepping procedure and tuning functions, have brought about the design of control systems with proven stability properties. In addition, there has been a lot of research activity on robust nonlinear control design methods, such as sliding mode control, Lyapunov redesign method, nonlinear damping, and adaptive bounding control. These techniques are based on the assumption that the uncertainty in the nonlinear functions is within some known, or partially known, bounding functions.
TABLE OF CONTENTS Chapter 1 - Introduction
This book presents adaptive function estimation and feedback control methodologies that develop and use approximations to portions of the nonlinear functions describing the system dynamics while the system is in online operation. Such methodologies have been proposed and analyzed under a variety of titles: neural control, adaptive fuzzy control, learning control, and approximation-based control. A primary objective of this text is to present the methods systematically in a unifying framework that will facilitate discussion of underlying properties and comparison of alternative techniques. This introductory chapter discusses some fundamental issues such as: (i) motivations for using adaptive approximation-based control; (ii) when adaptive approximation-based control methods are appropriate; (iii) how the problem can be formulated; and (iv) what design decisions are required. These issues are illustrated through the use of a simple simulation example. |
