Adaptive Iterative Learning Control for Non-Strictly Repeatable Systems With Unknown Control Gains

This work investigates the adaptive iterative learning control (AILC) problem for nonstrictly repeatable systems subject to unknown control gains. Different to the existing results, we novelly transform the unknown control gain into a kind of norm-bounded uncertainty, based on which a novel adaptive estimation approach is designed to reject the unknown control gain. Furthermore, to guarantee the learning ability of the controlled system subject to iteration-varying trial lengths, piecewise parametric update laws are proposed over the desired trial interval. Consequently, the proposed AILC strategy is then established by employing the error-tracking approach, which is capable of handling the iteration-varying initial states effectively. The convergence of the control algorithms is analyzed by applying the Lyapunov-like theory, and two numerical examples are illustrated to verify the proposed control scheme.