Repetitive learning control for remote control systems

Abstract

In this thesis, a Repetitive Learning Control (RLC) approach is proposed for a class of remote control nonlinear systems satisfying the global Lipschitz condition. The proposed approach is to deal with the remote tracking control problem when the environment is periodic over the infinite time domain. Since there exists a time delay, tracking a desired trajectory through a remote controller is not an easy task. A predictor is designed on the controller side to predict, the future state of the nonlinear system based on the delayed measurements from the sensor. The convergence of the estimation error of the predictor is ensured. The gain design of the predictor applies linear matrix inequality - LMI techniques. The repetitive learning control law is designed based on the feedback error from the predicted state. The proof of the stability is based on a constructed Lyapunov function. By incorporating the predictor and the RLC controller, the system state tracks the desired trajectory independently of the influence of time delays. A numerical simulation example is shown to illustrate the effectiveness of the proposed approach.