An Improved Approach to Iterative Learning Control for Uncertain Systems

Abstract

For iterative learning control (ILC) algorithms to date, there is a fundamental tradeoff between plant model knowledge and convergence rate in the iteration domain. This article presents a new fast ILC (FILC) method that uses a novel error term in the ILC learning law based on techniques from sliding mode control (SMC). The input signal is guaranteed to remain bounded in the time and iteration domains and is insensitive to noise due to the unique structure of the FILC learning algorithm. Moreover, the FILC approach does not require end-user tuning of arbitrary gains, which is useful for uncertain systems with significant uncertainty. The stability and convergence properties for the FILC system are presented using the Lyapunov analysis techniques. Simulation and experimental system results on a manufacturing system compare FILC with the existing ILC techniques and demonstrate that FILC achieves improved iteration convergence while retaining stability when plant uncertainty is high.