Trajectory tracking control of ROVs considering external disturbances and measurement noises using ESKF-based MPC

Abstract

This paper proposes a tracking method aiming at trajectory tracking for a remotely operated underwater vehicle (ROV) under external disturbances and measurement noises. Firstly, the six-degree-of-freedom kinematics model and dynamics model of the ROV are proposed to derive the discrete-time varying nonlinear model, where the Euler method is used and the assumption that the center of gravity of the ROV coincides with the center of buoyancy is made. The external disturbance representing ocean current is explicitly considered in the form of velocities instead of forces in the dynamics model to avoid the problem that forces on the ROV caused by ocean current are difficult to measure. Secondly, an extended state based Kalman filter (ESKF) is constructed to estimate system states and external disturbances in the presence of measurement noises and the filter gain is automatically tuned by the Kalman filter technique, which can greatly improve the estimation accuracy. Thirdly, the ESKF-based model predictive control (MPC) controller is newly formulated, and an objective function under linear time-invariant (LTI) constraints is constructed based on the tracking error and the desired control input increment to ensure the accuracy and prevent damage to actuators. Finally, the performance of the proposed method is verified by numerical simulations.