Pareto optimization-based Iterative Learning Control

Iterative Learning Control (ILC) is a technique for improving the performance of processes which repeatedly perform a task defined over a finite interval. Traditional ILC is used to improve trajectory tracking across an entire cycle period. However, there exist applications (pick n' place, surveillance) in which only specific locations are of particular interest. For these applications, point-to-point ILC results in improved tracking at the selected points and enhanced controller flexibility between locations. The additional control freedom can be used to maximize the performance of additional performance metrics. Pareto optimization is a multi-objective approach in which two or more conflicting objectives exist. In this paper, the point-to-point ILC framework is reformatted into a pareto optimization-based ILC approach in which two or more performance metrics are incorporated into the controller design. The modified framework enables the controller to leverage the additional control flexibility from a point-to-point approach to maximize multiple performance objectives. Convergence and performance analysis for the novel control framework is presented. Simulation results validate the control framework and demonstrate trade-offs in the performance metrics as a function of controller design.