Pseudo-inverse based iterative learning control for nonlinear plants with disturbances

Learning control is a very effective approach for tracking control in processes occuring repetitively over a fixed interval of time. In the paper, the stable-inversion based learning controller as presented in Ghosh and Paden (1999) is extended to accomodate a general class of nonlinear, nonminimum phase plants with disturbances and initialization errors. The extension requires the computation of an approximate inverse of the linearized plant rather than the exact inverse. An advantage of this approach is that the output of the plant need not be differentiated. A bound on the asymptotic trajectory error is exhibited via a concise proof, and is shown to grow continuously with a bound on the disturbances. Simulation studies confirm that in the presence of bounded disturbances, the tracking error converges to a neighborhood of zero. The structure of the controller is such that the low frequency components of the trajectory converge faster than the high frequency components