Adaptive Neural Consensus Control of Nonlinear Multi-agent Systems with Actuator Failures

Abstract

The adaptive consensus control for multi-agent systems (MASs) with actuator failures is considered in this article. By combining the neural networks (NNs) technique to develop the control scheme, the unknown nonlinear function are allowed to exist in the system dynamics. Moreover, the disturbance is also compensated by adaptive estimated parameter. The controller is totally distributed and only two unknown parameters needed to be updated. The presented control method not only ensures that every agent of MAS can track the leader with a predetermined error, but improves the transient performance. At last, a physical example is provided to demonstrate the effectiveness of the proposed method.