Prediction-based Iterative Learning Control (PILC) for uncertain dynamic nonlinear systems using system identification technique

Prediction-based Iterative Learning Control (PILC) is proposed in this pape r for a class of time varying nonlinear uncertain systems. Convergence of P ILC is analyzed and the uniform boundedness of tracking error is obtained i n the presence of uncertainty and disturbances. It is shown that the learni ng algorithm not only guarantees the robustness, but also improves the lear ning rate despite the presence of disturbances and slowly varying desired t rajectories in succeeding iterations. The effectiveness of the proposed PIL C is presented by simulations.