Quantized iterative learning control for singular nonlinear fractional-order time-delay multi-agent systems with iteration-varying reference trajectories and switching topologies
Xingyu Zhou, Haoping Wang, Kai Wang, Yang Tian
Abstract
This paper addresses the consensus tracking of the leader-following singular nonlinear fractional-order system with state time-delay in face of iteration-varying communication topologies and reference trajectories via an iterative learning control scheme. Firstly, by means of the neighbor information, the closed-loop D α -type quantized iterative learning protocol is constructed based on output tracking errors. Secondly, sufficient conditions of consensus errors between each follower agent and leader agent are given in a fixed time interval and convergence analysis is also presented under the fixed topology and reference trajectory. Thirdly, the extension to iteration-varying reference trajectory tracking and switching topologies cases are investigated. It is shown that the developed protocol can also effectively work along the iteration axis on a finite time interval , although the communication topologies and reference trajectories vary dynamically with respect to iteration. Finally, numerical examples are shown to verify the effectiveness of the obtained results.