Adaptive Finite-Time Optimal Formation Control for Second-Order Nonlinear Multiagent Systems

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.