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.

翻译：多智能体系统中的一致性控制已在多个领域得到广泛关注和实际应用。然而，在未知动力学条件下管理一致性控制仍是控制设计中的重大挑战，其原因在于系统不确定性和环境干扰。本文提出了一种新颖的基于学习的分布式控制律，并通过辅助动力学进行增强。利用高斯过程来补偿多智能体系统中的未知成分。为了持续提升高斯过程模型的预测性能，提出了一种具有分散事件触发机制的数据高效在线学习策略。此外，基于预测误差界概率保证，通过李雅普诺夫理论确保了所提方法的控制性能。为了验证所提出的基于学习的控制器的有效性，进行了对比分析，将其与传统的分布式控制律和离线学习方法进行了比较。