Iterative learning control for nonlinear stochastic systems with variable pass length

This paper deals with the iterative learning control (ILC) problem for a class of repetitive systems with nonlinear stochastic dynamics and variable pass length. The concept of recursive interval Gaussian distribution is first introduced to describe randomness of the pass lengths. By developing a modified iteration-average operator, a novel ILC scheme is proposed to overcome the limitation of conventional ILC algorithms that every pass must end in a fixed time of duration throughout the repetition. It is shown that for the nonlinear time-varying stochastic system, the proposed ILC approach works effectively to guarantee boundedness of the tracking error. Finally, a simulation study on a practical injection molding machine is provided to demonstrate the effectiveness and merits of the proposed method. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.