Neural network observer-based networked control for a class of nonlinear systems

Abstract

A new neural network observer-based networked control structure for a class of nonlinear systems is developed and analyzed. The structure is divided into three parts: local linearized subsystem, communication channels and remote predictive controller. A neural-network-based adaptive observer is presented to approximate the state of the time-delay-free nonlinear system. The neural-network (NN) weights are tuned on-line and no exact knowledge of nonlinearities is required. The time delays considered in the forward and backward communication channels are constant and equal. A modified Smith predictor is proposed to compensate the time delays. The controller is designed based on the developed NN observer and the proposed Smith predictor. By using the Lyapunov theory, rigorous stability proofs for the closed-loop system are presented. Finally, simulations are performed and the results show the effectiveness of the proposed control strategy.