Event-Triggered Saturated Adaptive Iterative Learning Control of Nonlinear Fractional-Order Multi-Agent Systems

This paper studies the event-triggered saturated adaptive iterative learning control (ETSAILC) problem for fractional-order multi-agent systems (FOMASs) subject to local Lipschitz nonlinearities and input saturation. First, a mechanism is proposed ensure that events occur along an iteration axis, all follower agents synchronously broadcast their states at each triggering step, receive restore information from neighbors. Next, ETSAILC protocol designed condition presented. Then, composite energy function (CEF) with form of integral constructed utilized convergence analysis process, thereby boundedness closed-loop signals proved sufficient guaranteeing perfect consensus obtained. It first time extend CEF method into field systems. Finally, compared existing protocols, simulations demonstrate controller can effectively handle saturation obtain consensus. Meanwhile, number update amount transmitted are obviously reduced.