Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression

• URL: http://arxiv.org/abs/2402.03174v1
• Date: Mon, 5 Feb 2024 16:41:17 GMT
• Title: Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression
• Authors: Xiaobing Dai, Zewen Yang, Mengtian Xu, Fangzhou Liu, Georges Hattab and Sandra Hirche
• Abstract summary: This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. For continuous enhancement in predictive performance, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. 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.
• Score: 3.405252606286664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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