This article investigates the leader–follower formation learning control (FLC) problem for discrete-time strict-feedback multiagent systems (MASs). The objective is to acquire the experience knowledge from the stable leader–follower adaptive formation control process and improve the control performance by reusing the experiential knowledge. First, a two-layer control scheme is proposed to solve the leader–follower formation control problem. In the first layer, by combining adaptive distributed observers and constructed $i_{n}$ -step predictors, the leader’s future state is predicted by the followers in a distributed manner. In the second layer, the adaptive neural network (NN) controllers are constructed for the followers to ensure that all the followers track the predicted output of the leader. In the stable formation control process, the NN weights are verified to exponentially converge to their optimal values by developing an extended stability corollary of linear time-varying (LTV) system. Second, by constructing some specific “learning rules,” the NN weights with convergent sequences are synthetically acquired and stored in the followers as experience knowledge. Then, the stored knowledge is reused to construct the FLC. The proposed FLC method not only solves the leader–follower formation problem but also improves the transient control performance. Finally, the validity of the presented FLC scheme is illustrated by simulations.

中文翻译：

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离散时间非线性多智能体系统的 Leader-Follower 编队学习控制

本文研究了离散时间严格反馈多智能体系统 (MAS) 的领导者-追随者编队学习控制 (FLC) 问题。目标是从稳定的领航者-跟随者自适应编队控制过程中获取经验知识，并通过重用经验知识来提高控制性能。首先，提出了一种双层控制方案来解决领导者-跟随者编队控制问题。在第一层，通过结合自适应分布式观察者和构造 $i_{n}$ -步预测器，领导者的未来状态由追随者以分布式方式预测。在第二层中，为跟随者构建自适应神经网络 (NN) 控制器，以确保所有跟随者跟踪领导者的预测输出。在稳定编队控制过程中，通过开发线性时变 (LTV) 系统的扩展稳定性推论，验证 NN 权重以指数方式收敛到其最优值。其次，通过构建一些特定的“学习规则”，综合获取具有收敛序列的神经网络权重，并将其作为经验知识存储在追随者中。然后，重新使用存储的知识来构建 FLC。所提出的 FLC 方法不仅解决了领导者-跟随者形成问题，而且提高了瞬态控制性能。最后，