Adaptive Finite-Time Neural Consensus Tracking for Second-Order Nonlinear Multiagent Systems with Unknown Control Directions

Abstract

This paper mainly studies the problem of consensus tracking for second-order nonlinear multiagent system with unknown control directions. The backstepping technology based on command filtered can deal with the problem that the output of each agent can track the leaders state with acceptable accuracy in finite-time and the issue of computational explosion has also been well solved at the same time. The unknown nonlinear dynamics in the system are approximated using neural network. We define the error compensation signal to compensate the error generated by filtering. The Nussbaum type function is introduced to solve the problem of unknown control directions. The virtual control signal, control input signal and adaptive law are also designed respectively. A numerical example is given in the simulation to show the superiority of this scheme.