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In the realm of the cooperative control of multi-agent systems (MASs) withunknown dynamics, Gaussian process (GP) regression is widely used to infer theuncertainties due to its modeling flexibility of nonlinear functions and theexistence of a theoretical prediction error bound. Online learning, whichinvolves incorporating newly acquired training data into Gaussian processmodels, promises to improve control performance by enhancing predictions duringthe operation. Therefore, this paper investigates the online cooperativelearning algorithm for MAS control. Moreover, an event-triggered data selectionmechanism, inspired by the analysis of a centralized event-trigger, isintroduced to reduce the model update frequency and enhance the dataefficiency. With the proposed learning-based control, the practical convergenceof the MAS is validated with guaranteed tracking performance via the Lynaponvetheory. Furthermore, the exclusion of the Zeno behavior for individual agentsis shown. Finally, the effectiveness of the proposed event-triggered onlinelearning method is demonstrated in simulations.

Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version