ADP Based Fault-Tolerant Tracking Control for Underactuated AUV with Actuators Faults via Neural Network Observer

Abstract In this work, the fault-tolerant tacking control issue of underactuated autonomous underwater vehicle (AUV) with actuators faults is investigated. Firstly, an output-feedback error tacking system is constructed based on the theoretical model of underactuated AUV with actuators faults. Then, an adaptive dynamic programming (ADP) based fault-tolerant control controller is developed. In our proposed control scheme, a neural-network observer is designed to approximate the system states with actuators faults. A novel ADP scheme is constructed with critic neural network and action neural network in order to reduce the jitter in the control input and improve the tracking accuracy. Based on Lyapunov approach, the stability of the error tracking system is guaranteed by the proposed controller. At last, the simulation results show that the underactuated AUV achieves better tracking performance.