For single agent systems, probabilistic machine learning techniques such as Gaussian process regression have been shown to be suitable methods for inferring models of unknown nonlinearities, which can be employed to improve the performance of control laws. While this approach can be extended to the cooperative control of multiagent systems, it leads to a decentralized learning of the unknown nonlinearity, i.e., each agent independently infers a model. However, decentralized learning can potentially lead to poor control performance, since the models of individual agents are often accurate in merely a small region of the state space. In order to overcome this issue, we propose a novel method for the distributed aggregation of Gaussian process models, and extend probabilistic error bounds for Gaussian process regression to the proposed approach. Based on this distributed learning method, we develop a cooperative tracking control law for leader_follower consensus of multiagent systems with partially unknown, higher order, control-affine dynamics, and analyze its stability using the Lyapunov theory. The effectiveness of the proposed methods is demonstrated in numerical evaluations.

中文翻译：

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一类具有SISS逆动力学的随机非线性时滞系统的全局镇定


对于单智能体系统，概率机器学习技术（例如高斯过程回归）已被证明是推断未知非线性模型的合适方法，可用于提高控制律的性能。虽然这种方法可以扩展到多智能体系统的协作控制，但它会导致未知非线性的分散学习，即每个智​​能体独立地推断一个模型。然而，分散学习可能会导致控制性能不佳，因为单个代理的模型通常仅在状态空间的一小部分区域内是准确的。为了克服这个问题，我们提出了一种用于高斯过程模型的分布式聚合的新方法，并将高斯过程回归的概率误差界扩展到所提出的方法。基于这种分布式学习方法，我们开发了一种用于部分未知、高阶、控制仿射动力学的多智能体系统的leader_follower共识的协作跟踪控制律，并使用Lyapunov理论分析了其稳定性。数值评估证明了所提出方法的有效性。