Event-triggered cooperative learning from output feedback control for multi-agent systems

Abstract

Abstract In this paper, the event-triggered distributed cooperative learning from output feedback control is presented for a group of uncertain nonlinear systems whose structures are identical but their reference signals are different. An event-triggered communication scheme is used in the control process to overcome the disadvantages of continuous communication. Meanwhile, the weight estimates of all neural networks (NNs) also converge to a small neighborhood of their optimal values, and the generalization ability of NNs is well guaranteed. Specifically, the trigger condition of each agent is only dependent on its own NN weight estimate. It is proved that the inter-event times are lower bounded by a positive constant to avoid the accumulation of events. Finally, a numerical example is provided to substantiate the proposed scheme.