Optimal iterative learning control with uncertain reference points

In this paper, we present two iterative learning control (ILC) frameworks for multiple points tracking problems. First, we present an ILC scheme to produce output curves that pass close to the reference points without considering the reference trajectory. Here, the control signals are generated by solving an optimal ILC problem with respect to the points. Second, we propose an optimal ILC multiple points tracking technique to handle non-repetitive uncertainties at reference points, which happens naturally in real applications due to noise contamination, disturbances, and other control purpose. As a result, the problem is formulated as a two-objective optimization problem.