This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inter-agent correlations for uncertainty predictions. A standout feature is its exceptional efficiency in deriving the aggregation weights achieved by circumventing the computationally intensive posterior variance calculations. Through Lyapunov stability analysis, the distributed control law ensures bounded tracking errors with high probability. Simulation experiments validate the protocol's efficacy in effectively managing complex scenarios, establishing it as a promising solution for robust tracking control in multi-agent systems characterized by uncertain dynamics and dynamic communication structures.

翻译：本文提出了一种创新的基于学习方法，用于解决在切换通信拓扑下运行、具有部分未知动力学的欧拉-拉格朗日多智能体系统的跟踪控制问题。该方法利用基于高斯过程回归构建的考虑相关性的协同算法框架，该框架能够巧妙地捕捉智能体间的不确定性预测相关性。其显著特点在于通过规避计算密集的后验方差计算，在推导聚合权重方面实现了卓越效率。通过李雅普诺夫稳定性分析，所提出的分布式控制律能以高概率确保有界跟踪误差。仿真实验验证了该协议在有效管理复杂场景中的效能，使其成为以不确定动力学和动态通信结构为特征的多智能体系统中鲁棒跟踪控制的一种有前景的解决方案。