Adaptive learning control for nonminimum phase systems

A novel adaptive learning algorithm is presented for the repetitive tracking control of a class of unstable nonminimum phase systems. After each repetitive trial, a least squares method is used to estimate the system parameters. The output tracking error and the identified system model are used through stable inversion to find the feed forward input, together with the desired state trajectories for the next trial. An adaptive backstepping based tracking controller is used in each trial to ensure the regulation of the desired state trajectories. Simulation results demonstrate that the proposed learning control scheme is very effective in reproducing the desired trajectories.