Bionic Underwater Vehicle: A Data-Driven Disturbance Rejection Control Framework

Disturbances caused by unknown dynamics and environmental factors render the automatic control of underwater vehicles extremely challenging. These effects are complex, time varying, and difficult to model accurately, leading to possible instability in the control process. This article focuses on the disturbance rejection problem of an underactuated bionic underwater vehicle (BUV). A novel control approach that combines the robustness of a model predictive controller with the modeling capabilities of a learning-based observer is proposed. More specifically, a practical Gaussian process (GP)-based disturbance observer is designed for online disturbance estimation. The interactive prediction results are integrated into a model predictive controller to obtain a refined dynamics model. The control framework optimally solves for the optimal control output to achieve control at the dynamics level. The proposed approach realizes disturbance rejection control while ensuring real-time application, constraint satisfaction, and recursive feasibility under complicated disturbances. The feasibility and efficiency of the proposed approach are verified through simulations and real-world experiments.