Robust Tracking of Nonrepetitive Learning Control Systems With Iteration-Dependent References

Abstract

Typically, iterative learning control (ILC) is applied based on a core hypothesis that the strict repetitiveness of control environment, task, and model should be satisfied by the controlled system. The problem of interest in this paper is: whether and how can ILC robustly work for controlled systems subject to iteration-dependent environments, tasks and models? To successfully solve this problem, an ILC algorithm using a high-order internal model (HOIM) is proposed and convergence conditions are developed. It is shown that HOIM-based ILC both possesses robustness against iteration-dependent uncertainties from initial states, disturbances, and plant models and tracks iteration-dependent references. Also, simulation tests validate the effectiveness of HOIM-based ILC.