Abstract
An adaptive iterative learning control (ILC) algorithm based on an estimation procedure using a Kalman filter and an optimization of a quadratic criterion is presented. It is shown that by taking the measurement disturbance into consideration the resulting ILC filters become iteration-varying. Results from experiments on an industrial robot show that the algorithm is successful also in an application.

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