Adaptive Finite-Time Optimal Formation Control for Second-Order Nonlinear Multiagent Systems
针对未知非线性动力学的二阶多智能体系统，提出一种自适应反步有限时间最优编队控制方法，利用神经网络辨识未知项，并设计含指数幂项的最优性能指标函数，在有限时间内实现有界信号和最小成本编队控制。
This article addresses an adaptive backstepping finite-time optimal formation control problem for second-order multiagent systems (MASs) with unknown nonlinear dynamics. Neural networks (NNs) are used to identify the unknown uncertain terms in the controlled system. Then, the finite-time optimal formation control is designed by constructing a novel optimal performance index function containing exponential power terms based on the framework of identifier–actor–critic. It is proved that all signals in the system are bounded in finite time, and the formation control is simultaneously achieved at minimum cost. The effectiveness and superiority of the proposed control algorithm are verified by simulation comparisons and data analyses.