MPC Based Trajectory Tracking for An Automonous Deep-Sea Tracked Mining Vehicle

Model predictive control (MPC) has been successfully used in trajectory tracking for autonomous vehicles based on certain kinematic model under low external disturbance conditions, but when there are model uncertainties and external disturbances, autonomous vehicles will fail to follow the pre-set trajectory. This paper studies trajectory tracking control based on MPC for an autonomous deep-sea tracked mining vehicle in polymetallic nodule mines with model uncertainty and external disturbances. A MPC algorithm is designed for trajectory tracking. To address model uncertainties caused by vehicle body subsidence and track slippage, a drive wheel speed correction controller is designed by experimental data fitting, and Kalman filtering (KF) and adaptive Kalman filtering (AKF) are introduced to improve tracking performance by rejecting external disturbances especially during curve tracking. To handle dead zones and obstacles during actual operation, an obstacle avoidance strategy is proposed that uses the tri-circular arc obstacle avoidance trajectory with an equal curvature for path re-planning. Finally, Simulink&Recurdyn co-simulations validate the performance of the proposed MPC controller through a comparison with nonlinear MPC(NMPC).