A New Predictor-based Repetitive Learning Control Approach for a Class of Remote Control Nonlinear Systems

In this paper, a repetitive learning control (RLC) approach is proposed for a class of remote control nonlinear systems satisfying the global Lipschitz condition. Since there exist time delays in the two transmission channels, tracking a desired trajectory through a remote controller is not an easy task. In order to solve this problema 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 developed by Lyapunov Kravoskii method for time delay systems. The repetitive learning control law is constructed based on the feedback error from the predicted state. The overall tracking error tends to zero asymptotically over iterations. A numerical simulation example is shown to verify the effectiveness of the proposed approach.