Iterative learning control for nonlinear nonminimum phase plants with input disturbances

Learning control is a very effective approach for tracking control in processes occuring repetitively over a fixed interval of time. In the paper a robust learning algorithm is proposed for a generic family of nonlinear, nonminimum phase plants with input disturbances. The stable-inversion based learning controller for linear plants, as proposed by Gao and Chen (1998), is adapted to accomodate the above more general class of plants. The bounds on the asymptotic error for the learned input are exhibited via a concise proof. Simulation studies demonstrate that in the absence of input disturbances perfect tracking of the desired trajectory is achieved for nonlinear nonminimum phase plants. Further, in the presence of bounded random input disturbances, the tracking error converges to a neighborhood of zero. A bound on the tracking error is derived which is shown to grow continuously with a bound on the disturbances