Robust MPC-based trajectory tracking of autonomous underwater vehicles with model uncertainty.

A robust model predictive control (MPC) method with dual closed-loops is presented to handle trajectory tracking of autonomous underwater vehicle (AUV) with uncertain model parameters and random external perturbations. First, constraint conditions are set for the motion state and control input of the underwater vehicle based on its motion characteristics. The position controller takes the velocity increment as input, thus providing a smoothly varying desired velocity for the velocity controller. The velocity controller comprises nominal MPC and a nonlinear auxiliary control law to overcome the effect of random perturbations on AUV tracking control. Then, a finite-time extended state observer (FTESO) is designed to compensate for dynamic model uncertainty. Furthermore, Lyapunov stability theory is employed to analyse the stability of the controller and FTESO. Ultimately, through comparative simulation experiments, the proposed control framework's effectiveness and robustness are verified, proving it to be a feasible AUV trajectory tracking control method. • A dual closed-loop 3D trajectory tracking control framework based on tube MPC is designed. • A finite time extended state observer is designed to estimate and compensate for the uncertainty of the AUV dynamic model. • By designing a nonlinear auxiliary control law, the influence of external disturbances on AUV is solved, and the robustness of system is improved. • The stability and feasibility of the state observer and tube MPC-based controller are rigorously demonstrated. [ABSTRACT FROM AUTHOR]