Abstract
Methods of iterative learning control for discrete-time systems have already been proposed. However, the applicability of these schemes depends essentially on the structure of the system. There are systems to which these schemes cannot be applied, even if the system models are completely known. In this paper we present a scheme, based on the gradient method, of iterative learning control for linear discrete-time systems in order to overcome these difficulties. The applicability of this scheme depends only on the amount of uncertainty in the system parameters. Some convergence conditions expressed as ranges of parameters are also presented for practical application to systems with uncertainty.
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