Abstract
This paper studies the globally repetitive learning leader-following consensus for a class of unknown nonlinear multi-agent systems with uncertain periodic time-varying parameters. Neural networks are used as the feed-forward compensators to model some unknown nonlinear system functions. According to the different distributions of agents in the network topology, and by learning periodic uncertainties, a new hybrid repetitive learning control protocol is presented based on Lyapunov theory. Meanwhile, the proposed learning control protocol can guarantee that all the followers can track the leader asymptotically. Moreover, the boundedness of all the signals is ensured. Finally, three simulation examples are provided to verify the effectiveness of the proposed scheme.
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