Modelling, identification and stable adaptive control of continuous-time nonlinear dynamical systems using neural networks

Abstract

Several empirical studies have demonstrated the feasibility of employing neural networks as models of nonlinear dynamical systems. This paper develops the appropriate mathematical tools for synthesizing and analyzing stable neural network based identification and control schemes. Feedforward network architectures are combined with dynamical elements, in the form of stable filters, to construct a general recurrent network configuration which is shown to be capable of approximating a large class of dynamical systems. Adaptive identification and control schemes, based on neural network models, are developed using the Lyapunov synthesis approach with the projection modification method. These schemes are shown to guarantee stability of the overall system, even in the presence of modelling errors. A crucial characteristic of the methods and formulations developed in this paper is the generality of the results which allows their application to various neural network models as well as other approximators.