Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems

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.