Adaptive distributed fault-tolerant control for underactuated surface vehicles with bridge-to-bridge event-triggered mechanism

This paper investigates an adaptive distributed fault-tolerant formation control scheme for multiple underactuated surface vehicles (USVs) with the bridge-to-bridge event-triggered communication mechanism. In particular, under the directed topology, information from each USV is broadcast to the adjacent vehicles only if the event-triggered condition is satisfied. The benefit of the proposed event-triggered communication mechanism eliminates the requirement of either the continuous monitoring of adjacent vehicles' states or global graph information, which can address the problem of the limited communication bandwidth in the marine practice. Based on the event-triggered communication, backstepping and directed topology, an adaptive distributed formation controller is developed for each USV. Besides, the unknown structure uncertainty and external disturbances are compressed together to compensate by introducing the neural network (NN) approximator and the neural damping technique. And the derived adaptive law can also compensate the adverse effect of unknown actuator faults and gain uncertainty. Through the Lyapunov theory, all signals in the closed-loop system are proved to be with the semi-global uniform ultimate bounded (SGUUB) stability. Finally, the numerical simulation is illustrated to verify the effectiveness of the control algorithm.

Automated summary

This paper investigates an adaptive distributed fault-tolerant formation control scheme for multiple underactuated surface vehicles (USVs) with the bridge-to-bridge event-triggered communication mechanism. The proposed mechanism eliminates the requirement of continuous monitoring of adjacent vehicles' states or global graph information, addressing the limited communication bandwidth problem. An adaptive distributed formation controller is developed for each USV based on event-triggered communication, backstepping, and directed topology. Additionally, neural network approximation and neural damping technique are used to compensate for unknown structure uncertainty and external disturbances.