The fundamental processes involved in controlling a dynamical system include the mathematical modeling of the system, identification based on experimental data, processing of the outputs, and using them in turn to synthesize control inputs to achieve desired behavior. The aim of automatic control is to achieve the latter rapidly, accurately, and in a stable fashion. The problem of controlling a known linear plant with multiple inputs and multiple outputs is a difficult one. When the parameters of such systems are unknown, the problem is considerably more difficult, and adaptive control is needed. The difficulty is substantially grater when the plant is known but nonlinear. When the plant is nonlinear with unknown characteristics, we have a nonlinear adaptive control problem which is truly formidable. In numerous application in new areas such as space technology, manufacturing, and robotics, as well as established areas such as process control and aircraft control, such problems are arising with increasing frequency. The proposal deals with theoretical and practical aspects of the control of such systems using neural networks. The first part of this work will deal with the representation problem of non linear systems and the existence of controller for them. In the second part, questions related to the choice of neural networks as identifiers and controllers will be addressed. The third part will deal with the important question of stability. In the fourth part, a detailed study of control using multiple models will be carried on. Empirical studies carried out in the past three years suggest that such a control methodology based on multiple models is needed to cope efficiently with rapid changes in the environment. The four part of the proposal represents four of the important aspects of nonlinear adaptive control using neural networks.
