Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version
CoRR(2023)
摘要
In the realm of the cooperative control of multi-agent systems (MASs) with
unknown dynamics, Gaussian process (GP) regression is widely used to infer the
uncertainties due to its modeling flexibility of nonlinear functions and the
existence of a theoretical prediction error bound. Online learning, which
involves incorporating newly acquired training data into Gaussian process
models, promises to improve control performance by enhancing predictions during
the operation. Therefore, this paper investigates the online cooperative
learning algorithm for MAS control. Moreover, an event-triggered data selection
mechanism, inspired by the analysis of a centralized event-trigger, is
introduced to reduce the model update frequency and enhance the data
efficiency. With the proposed learning-based control, the practical convergence
of the MAS is validated with guaranteed tracking performance via the Lynaponve
theory. Furthermore, the exclusion of the Zeno behavior for individual agents
is shown. Finally, the effectiveness of the proposed event-triggered online
learning method is demonstrated in simulations.
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关键词
gaussian process regression,online learning,control,event-triggered,multi-agent
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