Event-triggered Iterative Learning Control for Perfect Consensus Tracking of Non-identical Fractional Order Multi-agent Systems

Abstract

This paper is devoted to the perfect tracking problem of output consensus for a class of non-identical fractional order multi-agent systems (NIFOMASs), in which different agents have different and unknown factional orders and dynamic functions. For the NIFOMASs including one leader agent and multiple follower agents, by designing the event-triggered mechanism along an iteration axis and introducing it into the iterative learning controller, an event-triggered iterative learning consensus protocol is proposed to reduce the number of controller update and to save the communication resource. By analyzing the convergence of learning process, the sufficient conditions are derived to guarantee that the output consensus tracking can be perfectly achieved over the finite time interval as the iteration step goes to infinity. Finally, three numerical examples are presented to demonstrate the effectiveness and wide application scope of the proposed control strategy.