LAUSR

This paper presents an event-triggered adaptive consensus algorithm to deal with the consensus problem for nonlinear multi-agent systems (MASs) with unknown control direction and actuator failures. The nonlinear dynamics and the disturbance considered in this paper are all unknown. The unknown nonlinear dynamics of MASs is approximated by the radial basis function neural networks (RBFNNs). Then, a disturbance observer with adaptive parameter is put forward to depress the total disturbances which contain the unknown external disturbances, RBFNNs approximation errors and bias faults. The proposed consensus algorithm enables that all closed-loop signals are bounded, and the followers’ states can track the leader’s. Finally, the proposed consensus algorithm is validated by simulations.