An Adaptive Weight Model Predictive Control Algorithm to Trajectory Tracking Control of UUV

Unmanned Underwater Vehicle (UUV) has been applied increasingly in marine work, and the trajectory tracking speed and accuracy directly affect work efficiency. To improve the convergence speed and accuracy of UUV tracking control, an adaptive weight model predictive control based on quantum particle swarm optimization (QPSO-AWMPC) is presented in this paper. The control weight is determined by the state tracking error. At the initial stage, where the tracking error is large, the weight control is lowered to achieve a higher speed. At the stable phase, where the tracking error is small, the weight control is raised to maintain robustness. A kinematics controller is designed to optimize state error and obtain the desired speed within constraints, while a dynamic controller is proposed to optimize speed and obtain the required thrust. Finally, the three-dimensional simulation verifies the effectiveness of the proposed method. Compared with the traditional MPC and Backstepping methods, the internal constraint of the UUV is satisfied while the convergence speed, accuracy, and robustness are improved.