Model reference iterative learning control

This paper introduces a new formulation of the iterative learning control (ILC): in this version, the states of a plant can be steered to follow the states of a reference model that does not necessarily have the same structure as the plant. In order to achieve such an objective, the designed ILC includes a stabilization term and an iteratively updated term, as a new control input. As long as the system parameters satisfy the plant-model matching conditions, the reference model can be followed successfully. Stability of the tracking-error is proven, and an application example to a circuit system is presented to illustrate the proposed model reference iterative learning control (MRILC).