Fault-Tolerant Model Predictive Control for Autonomous Underwater Vehicles Considering Unknown Disturbances

Abstract

This paper presents a fault-tolerant model predictive control approach for cross-rudder autonomous underwater vehicles to achieve heading control, considering rudder stuck faults and unknown disturbances. Specifically, additive faults in the rudders are addressed, and an active fault-tolerant control strategy is employed. Fault models of autonomous underwater vehicles have been established to develop the fault-tolerant control method. In the controller design, the stuck faults of complete rudder failure are incorporated to ensure the heading angle control of the autonomous underwater vehicle in faulty conditions. Furthermore, the fault term is decoupled from the control input, and the decoupled control input, along with corresponding constraints, is incorporated into the model’s predictive controller design. This approach facilitates controller reconfiguration, thereby enhancing and optimizing control performance. Simulation results demonstrate that the proposed fault-tolerant model predictive control method can effectively achieve stable navigation and heading adjustment under rudder fault conditions in autonomous underwater vehicles.