Data-Driven MPC for Attitude Control of Autonomous Underwater Robot

Abstract

High maneuverability is essential to the autonomous operation of underwater robots. To achieve real-time maneuvering motion, the control strategy must take into account nonlinear hydrodynamic effects, which are extremely difficult to accurately capture during motion and therefore a balance must be struck between accuracy and real-time computational efficiency. Therefore, this paper proposes a data-driven approach to model the dynamics of the underwater robot using Sparse Identification of Nonlinear Dynamics (SINDy). Compared with existing works, our method does not require any physical prior knowledge and only uses a short period of onboard sensor data. Subsequently, the learned dynamic model is incorporated into a model predictive controller (MPC) to enable precise attitude control. Finally, the proposed method is implemented on our developed fully vectored propulsion underwater robot, and a series of attitude tracking experiments are conducted in an indoor water tank. Experimental results reveal that our approach significantly improves the model accuracy and reduces the attitude tracking errors by over 79% at a control frequency of 20 Hz, which proves the effectiveness and real-time performance of the method.