Robust adaptive neural network integrated fault-tolerant control for underactuated surface vessels with finite-time convergence and event-triggered inputs

In this paper, we study the trajectory tracking control of underactuated surface vessels(USVs) subject to actuator faults, uncertain dynamics, unknown environmental disturbances, and communication resource constraints. Considering that the actuator is prone to bad faults, the uncertainties formed by the combination of fault factors, dynamic uncertainties and external disturbances are compensated by a single online updated adaptive parameter. In the compensation process, we combine the robust neural-damping technology with the minimum learning parameters (MLPs), which improves the compensation accuracy and reduces the computational complexity of the system. To further improve the steady-state performance and transient response of the system, finite-time control (FTC) theory is introduced into the design of the control scheme. At the same time, we adopt the event-triggered control (ETC) technology, which reduces the action frequency of the controller and effectively saves the remote communication resources of the system. The effectiveness of the proposed control scheme is verified by simulation. Simulation results show that the control scheme has high tracking accuracy and strong anti-interference ability. In addition, it can effectively compensate for the adverse influence of fault factors on the actuator, and save the remote communication resources of the system.