Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
<|reference_start|>Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression: Consensus control in multi-agent systems has received significant attention and practical implementation across various domains. However, managing consensus control under unknown dynamics remains a significant challenge for control design due to system uncertainties and environmental disturbances. This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. Gaussian processes are harnessed to compensate for the unknown components of the multi-agent system. For continuous enhancement in predictive performance of Gaussian process model, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. Furthermore, the control performance of the proposed approach is ensured via the Lyapunov theory, based on a probabilistic guarantee for prediction error bounds. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies.<|reference_end|>
@article{dai2024decentralized,
    title={Decentralized Event-Triggered Online Learning for Safe Consensus of
  Multi-Agent Systems with Gaussian Process Regression},
    author={Xiaobing Dai, Zewen Yang, Mengtian Xu, Fangzhou Liu, Georges Hattab
  and Sandra Hirche},
    journal={arXiv preprint arXiv:2402.03174},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03174},
    primaryClass={eess.SY cs.LG cs.SY}
}