Incorporation of experience in iterative learning controllers using locally weighted learning

A method of incorporating experience in iterative learning controllers is proposed in this paper. Importance of the selection of initial control input in the convergence of error is highlighted. It is proposed that if previous experience of the controller can be incorporated in the selection of the initial control input for a new desired trajectory tracking task, the convergence of error can be improved without modifying the structure of the controller. Therefore, the proposed method is very general and is applicable to most of the iterative learning control algorithms.

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