Iterative learning control of nonlinear non-minimum phase systems and its application to system and model inversion

We present a model based method for reference tracking in the iterative learning control (ILC) framework. The method can be applied to nonlinear, possibly non-minimum phase, systems. The idea is to use the inverse of a linearized model in the ILC update. In the non-minimum phase case, the batch property of ILC is explored by means of non-causal filtering. Apart from reference tracking, this method is useful for system and model inversion-a problem that arises in many disciplines where nonlinear systems and models are involved, e.g. maximum likelihood identification and input design for identification for control. The method is illustrated on a numerical example.