Indirect Adaptive Control Using Hopfield-Based Dynamic Neural Network for SISO Nonlinear Systems

Abstract

In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed. In addition. the tracking error can be attenuated to a desired level by selecting some parameters adequately. Simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.