Moving Averaging Techniques for Linear Discrete-Time Systems
To further improve the learning performance, this chapter will propose two novel ILC schemes for discrete-time linear systems with randomly varying trial lengths. In contrast to Chaps. 2 and 3 that advocate to replace the missing control information by zero, the proposed learning algorithms in this chapter are equipped with a random searching mechanism to collect useful but avoid redundant past tracking information, which could expedite the learning speed. The searching mechanism is realized by using the newly defined stochastic variables and an iteratively moving average operator. The convergence of the proposed learning schemes is strictly proved based on the contraction mapping methodology. Two illustrative examples are provided to show the superiorities of the proposed approaches.