Survey on stochastic iterative learning control

Abstract

Iterative learning control (ILC) is suitable for systems that are able to repeatedly complete several tasks over a fixed time interval. Since it was first proposed, ILC has been further developed through extensive efforts. However, there are few related results on systems with stochastic signals, where by stochastic signal we mean one that is described by a random variable. Stochastic iterative learning control (SILC) is defined as ILC for systems that contain stochastic signals including system noises, measurement noises, random packet losses, etc. This manuscript surveys the current state of the art in SILC from the perspective of key techniques, which are divided into three parts: SILC for linear stochastic systems, SILC for nonlinear stochastic systems, and systems with other stochastic signals. In addition, three promising directions are also provided, namely stochastic ILC for point-to-point control, stochastic ILC for iteration-varying reference tracking, and decentralized/distributed coordinated stochastic ILC, respectively.