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.

本研究提出了一种基于学习的方法，用于解决具有部分未知动力学的切换通信拓扑下的欧拉-拉格朗日多智能体系统的跟踪控制问题。该方法借助基于高斯过程回归的关联感知协同算法框架，巧妙地捕捉智能体间的相关性以进行不确定性预测。其显著特点是在避免了计算密集的后验方差计算的情况下，优化了汇聚权重的计算效率。通过李亚普诺夫稳定性分析，分布式控制律确保跟踪误差有很高的概率界限。仿真实验证实了该协议在有效管理复杂情况方面的功效，使其成为处理具有不确定动力学和动态通信结构的多智能体系统的鲁棒跟踪控制的有希望的解决方案。

基于高斯过程的欧拉-拉格朗日系统跟踪控制在拓扑切换下的合作学习