An inversion-based iterative learning control algorithm for a class of nonminimum-phase systems

In a new inversion-based iterative learning algorithm for a class of nonminimum-phase systems the least-squares method is used to estimate the system parameters after each repetitive trial. The output tracking error and the identified system model are used through stable inversion to find the feedforward input, together with the desired state trajectories, for the next trial. A robust controller is used in each trial to ensure the stability of the systems and the output tracking error convergence. Sufficient conditions for learning control convergence are provided. Simulation studies on systems with gain uncertainty and time constant uncertainty are also presented. Simulation results demonstrate that the proposed learning control scheme is effective in reproducing the desired trajectories.