Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method.

Automated summary

This paper investigates the human-in-the-Ioop leader-following consensus control problem in multi-agent systems with unknown matched nonlinear functions and actuator faults. A neural fault-tolerant controller with dynamic coupling gains is proposed by using neural networks and fault estimators. It is proved that the state of each follower can synchronize with the leader's state and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Simulation results are presented to demonstrate the effectiveness of the proposed control method.