Robust Adaptive Iterative Learning Control for Nonlinear Systems with Non-Repetitive Variables

In this work, the temporally and iteratively varying problems in iterative learning control for a class of nonlinear multiple input multiple output systems is discussed. Time-iteration-varying variables are generated by high-order internal models. Reference trajectories and system initial states are bounded and vary randomly in iteration domain. Then an operator is applied to update the estimation matrix for the whole uncertainties including non-repetitive parameters and time-varying disturbances. With the proposed adaptive iterative learning control technique, estimation error is bounded and tracking error converges to zero asymptotically. The effectiveness of the proposed control is verified through simulation study.