Fault‐Tolerant Tracking Control for Fully‐Coupled Nonaffine Autonomous Underwater With Actuator Faults Using Online‐Policy Reinforcement Learning

In recent years, autonomous underwater vehicle (AUV) has been widely used in various fields, which has attracted increasing attention from scholars for its motion control. In this study, we mainly address the fault‐tolerant tracking control problem of AUV with actuator faults. Because AUV system is a complex nonlinear system, it exhibits coupling and nonaffine properties. These features and actuator faults pose challenges to the fault‐tolerant tracking control of AUV. Considering the coupling and nonaffine properties and actuator faults, we use the mean value theorem to transform the fully coupled nonaffine AUV (FCNAUV) model into an affine AUV model. Different from offline reinforcement learning (RL), we proposed a novel online‐policy‐iteration reinforcement learning (OPIRL) to construct a fault‐tolerant tracking control scheme. Using a single‐layer critic network, the performance index function is approximated, which effectively reduces the computing burden in the online training process. In addition, a novel weight update law is designed to improve the algorithm efficiency. The simulation results show that the proposed method achieves better system performance and has a better convergence speed for FCNAUV with rudder faults and with propeller faults.