Abstract
This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to zero tracking error. If the plant does not satisfy the positivity condition, a linear LQ tracker can be used to condition the plant so that it satisfies the positivity condition. The overall structure results in a novel combination of Arimoto ILC and LQ optimal control, that drives the tracking error monotonically to zero for an arbitrary discrete-time LTI plant. This is a very strong property for any ILC algorithm.
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