Trajectory Tracking Control for Unmanned Underwater Vehicles via Robust Quasi-Linear Parameter-Varying Model Predictive Control Considering External Disturbances and Input Constraints

This paper introduces a quasi-linear parameter-varying model predictive control (qLPV MPC) framework for robust trajectory tracking of fully actuated unmanned underwater vehicles (UUVs), explicitly considering external disturbances and input constraints. To address the challenges of nonlinear dynamics, actuator limitations, and external disturbances, the UUV system is formulated as a qLPV model to balance the computational efficiency and the control accuracy. Then, a qLPV MPC method is designed for UUVs by incorporating an auxiliary feedback control law and a terminal constraint set, which ensures iterative feasibility and asymptotic stability. The input-to-state stability (ISS) is mathematically proven through Lyapunov-based analysis, so that the proposed framework could stabilize bounded tracking errors despite system disturbances. Finally, an iterative solution method based on sequential quadratic programming (SQP) is introduced to obtain efficient solutions of the optimal control sequence while reducing the computational complexity. Simulation studies are conducted to validate the effectiveness of the proposed controller. The results show that the proposed method exhibits smaller tracking errors and faster convergence speeds compared to traditional robust nonlinear model predictive control (NMPC).