Event-Triggered Adaptive Neural Finite-Time Trajectory Tracking Control of Underactuated Surface Vessels with Input Saturation*

Abstract

To eliminate the adverse effects of dynamic uncertainty and unknown external disturbances on the underactuated surface vessels(USVs) trajectory tracking system under the input saturation, a concise trajectory tracking control scheme is designed in this paper. The dynamic uncertainty of the USVs is reconstructed by using the neural networks(NNs), and the minimum learning parameters(MLPs) is introduced to convert the uncertain dynamic of the system into a single-parameter learning form, which reduces the computational complexity of the system. The transient performance and steady-state performance of the system are improved by finite-time technology, and event-triggered technology is introduced to solve the impact of communication resource limitation on the system. A rigorous theoretical analysis is provided for the designed control scheme based on Lyapunov stability theory, and the effectiveness of the control scheme is verified by simulation. The simulation results show that the control scheme designed in this paper has good steady-state performance and high tracking accuracy, and effectively saves communication resources.