Leader–Follower Formation Learning Control of Discrete-Time Nonlinear Multiagent Systems
研究了离散时间非线性多智能体系统的领导者-跟随者编队学习控制问题，通过两层控制方案获取经验知识并重用，以提升控制性能。
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i_{n}$ </tex-math></inline-formula> -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.