Iterative learning and repetitive controller design via duality with experimental verification

A dual relationship has been shown to exist between iterative learning and repetitive control, in which both control paradigms differ only in the location of an internal model of the disturbance. In this paper it is shown how the framework may be applied to derive new controllers which can be configured in either current error feedback or past error feedforward structures, which may assume the form of either a servomechanism or disturbance observer/compensator. Stability conditions demonstrate that the new schemes increase the set of plants which can be controlled within the framework.