Iterative Learning-Based Data-Driven Control for Unknown Unmanned Marine Vehicles with Actuator Faults

This paper researches the iterative learning-based data-driven control for unknown unmanned marine vehicles with actuator faults, while considering the trajectory tracking problem in the presence of external disturbances. Using the partial form dynamic linearization technique, we establish an equivalent dynamic linearization data model with independent disturbance of unmanned marine vehicles. The disturbance and input data have different pseudo partial derivative matrices, which increases the immunity of the UMVs to disturbance in the environment. Additionally, we propose an equivalent data model with unknown pseudo partial derivative matrices that includes both actuator faults and UMV dynamics information. And, an iterative learning-based fault-tolerant trajectory tracking data-driven control scheme is developed such that the error of trajectory tracking is convergent under external disturbance and actuator faults. Finally, the examples are provided to validate the effectiveness of the proposed iterative learning trajectory tracking control scheme in this paper.