Adaptive Neural Fault-Tolerant Control for USV With the Output-Based Triggering Approach
Guoqing Zhang, Shengjia Chu, Weidong Zhang, Cheng Liu
Abstract
This paper presents an adaptive neural fault-tolerant control algorithm for the path-following activity of the underactuated surface vehicle (USV) using the novel output-based triggering approach. In the algorithm, the event-triggered mechanism is designed utilizing the attitude states from the kinematics aspect of USV. Both the control inputs and the related calculation thread are implemented only at the trigger instants. Furthermore, neural networks (NNs) are employed to remodel the model uncertainties, and the adaptive observer is developed to estimate and compensate for the effect of the unknown actuator faults. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system in aspects of both the trigger instant and the continuous interval. The comparison experiment has been illustrated to verify the effectiveness of the proposed algorithm, which can effectively improve the information transmission performance and the fault-tolerant capability.