Robust iterative learning control with high-order internal models for SISO nonrepetitive systems

In this paper, we address robust iterative learning control (ILC) problem for nonrepetitive systems subject to iteration-varying desired references generated by high-order internal models (HOIM). A modified high-order ILC algorithm is proposed by incorporating HOIM into the ILC algorithm design. We give one condition to guarantee the bounded system trajectories and tracking errors under the assumption that all system matrices, initial states, learning gain coefficients and iteration-varying reference trajectories are bounded. Furthermore, if the variations of all system matrices between two successive iterations converge to zeros and the initial state at each iteration satisfies the HOIM progressively, we give one additional condition together with the former one to guarantee the perfect zero-error tracking.