Iterative Learning Control for Nonlinear Systems: A Bounded-Error Algorithm

This paper presents a nonlinear iterative learning control (NILC) for nonlinear time-varying systems. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory-tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. A special feature of the algorithm is that the trial-time interval is finite but not fixed as it is for the other iterative learning algorithms. A sufficient condition for convergence and robustness of the bounded-error learning procedure is derived. With respect to the bounded-error and standard learning processes applied to a virtual robot, simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control.