Yu Xian, Chen Tianshi
IEEE Trans Cybern. 2024 Aug;54(8):4489-4501. doi: 10.1109/TCYB.2023.3281479. Epub 2024 Jul 18.
When applied to the consensus tracking of repetitive leader-follower multiagent systems (MASs), most of existing distributed iterative learning control (DILC) methods assume that the dynamics of agents are exactly known or up to the affine form. In this article, we study a more general case where the dynamics of agents are unknown, nonlinear, nonaffine, and heterogeneous, and the communication topologies can be iteration-varying. More specifically, we first apply the controller-based dynamic linearization method in the iteration domain to obtain a parametric learning controller using only the local input-output data collected from neighboring agents in a directed graph, and then propose a data-driven distributed adaptive iterative learning control (DAILC) method through the parameter-adaptive learning methods. We show that for each time instant, the tracking error is ultimately bounded in the iteration domain for both of the cases with iteration-invariant and iteration-varying communication topologies. The simulation results show that the proposed DAILC method has faster convergence speed, higher tracking accuracy, and more robust learning and tracking in comparison with a typical DAILC method.
当应用于重复的领导者-跟随者多智能体系统（MASs）的一致性跟踪时，大多数现有的分布式迭代学习控制（DILC）方法都假定智能体的动力学是精确已知的或至多为仿射形式。在本文中，我们研究一种更一般的情况，即智能体的动力学是未知的、非线性的、非仿射的且异构的，并且通信拓扑可以随迭代变化。更具体地说，我们首先在迭代域中应用基于控制器的动态线性化方法，以仅使用从有向图中的相邻智能体收集的局部输入-输出数据来获得参数学习控制器，然后通过参数自适应学习方法提出一种数据驱动的分布式自适应迭代学习控制（DAILC）方法。我们表明，对于每个时刻，在通信拓扑不变和随迭代变化这两种情况下，跟踪误差在迭代域中最终都是有界的。仿真结果表明，与典型的DAILC方法相比，所提出的DAILC方法具有更快的收敛速度、更高的跟踪精度以及更强的学习和跟踪鲁棒性。
