Adaptive neural finite-time bipartite consensus tracking of nonstrict feedback nonlinear coopetition multi-agent systems with input saturation

Abstract This paper concentrates on the adaptive neural finite-time bipartite consensus tracking of nonstrict feedback nonlinear coopetition multi-agent systems with input saturation. A novel consensus tracking method combined the adaptive neural control with the finite-time command filtered backstepping is proposed. During each backstepping process, the Radical Basis Function Neural Network (RBF NN) is used to approximate the unknown nonlinear dynamics and the finite-time sliding mode differentiator (FTSMD) is used to obtain intermediate signals and their derivative. Moreover, the filtering errors are eliminated by using error compensation signals. By using the finite-time Lyapunov stability theory, it can be proved that the bipartite consensus tracking errors can converge to a sufficient small region of the origin in finite-time and all signals in the closed-loop systems are bounded in finite-time although there exists the input saturation. The effectiveness of the proposed method is shown by simulation results.