ITERATIVE LEARNING CONTROL USING OPTIMAL FEEDBACK AND FEEDFORWARD ACTIONS

An algorithm for iterative learning control is developed on the basis of an optimization principle which has been used previously to derive gradient-type algorithms. The new algorithm has numerous benefits whic h include realization in terms of Riccati feedback and feedforward com ponents. This realization also has the advantage of implicitly ensurin g automatic step size selection and hence guaranteeing convergence wit hout the need for empirical choice of parameters. The algorithm is exp ressed as a very general norm optimization problem in a Hilbert space setting and hence, in principle, can be used for both continuous and d iscrete time systems. A basic relationship with almost singular optima l control is outlined. The theoretical results are illustrated by simu lation studies which highlight the dependence of the speed of converge nce on parameters chosen to represent the norm of the signals appearin g in the optimization problem.