Research And Application Of Predictive Control For Nonlinear Systems Based On Neural Network

This paper presents a predictive control based on BP-neural-network for a class of nonlinear discrete-time systems. The neural network predictive control law is developed through the minimization of a generalized predictive performance criterion. A real-time adaptive control algorithm, including a neural network predictor and a neural network predictive controller, is proposed; the adaptive learning laws for both the neural predictor and controller are determined based on Lyapunov stability theory. In this paper, the mode and the on-line recursive learning algorithm of the predictor and the controller are explicated in detail. The paper provides the range of the adaptive learning rates for both the predictor and the controller. Simulation results show that the proposed control algorithm can give satisfactory tracking performance for an illustrative nonlinear system.