Iterative learning control for impulsive multi-agent systems with varying trial lengths

Abstract

In this paper, we introduce iterative learning control (ILC) schemes with varying trial lengths (VTL) to control impulsive multi-agent systems (I-MAS). We use domain alignment operator to characterize each tracking error to ensure that the error can completely update the control function during each iteration. Then we analyze the system’s uniform convergence to the target leader. Further, we use two local average operators to optimize the control function such that it can make full use of the iteration error. Finally, numerical examples are provided to verify the theoretical results.

Convergence properties concerning Lebesgue‐p norm of iterative learning control for a class of fractional differential systems

“…Under the design framework of ILC, the system has to accomplish its control task in a fixed time interval and then repeat it again and again. In this context, many significant and interesting works have been reported for linear or nonlinear systems [5][6][7][8][9][10][11][12]. It should be pointed out that all of these works are for integer-order systems.…”