Current iteration tracking error assisted iterative learning control of uncertain nonlinear discrete-time systems

A simple iterative learning controller (ILC) is proposed for the tracking control of uncertain discrete-time nonlinear systems performing the repetitive tasks. The tracking error of the current learning iteration is utilized in the ILC updating law. It is proven that, under relaxed conditions, the final tracking error is bounded in the presence of uncertainty, disturbance and the initialization error. Furthermore, the tracking error bound and the ILC convergence rate can be tuned by the learning gain of the current iteration tracking error in the ILC updating law. The effectiveness of the proposed ILC scheme is illustrated by a simulation.