Adaptive Fast Finite-Time Stabilization of High-Order Uncertain Nonlinear Multi-Agent Systems

In this paper, a fast finite-time control problem for a class of high-order nonlinear multiagent systems (MASs) is researched. In the control design, radial basis function neural networks (RBFNNs) are used to approximate the unknown nonlinear dynamics, and an adaptive fast finite-time control method is developed in the framework of adaptive backstepping recursive design. Then, the constructed new integral-type Lyapunov function and the incorporation of the power integrator technique demonstrate the fast finite-time stability and convergence of the consensus tracking error of the closed-loop system.