Lyapunov-based model predictive control trajectory tracking for an autonomous underwater vehicle with external disturbances

This paper focus on the trajectory tracking of autonomous underwater vehicles (AUVs) in complex ocean environments. A novel Lyapunov-based Model Predictive Control (LMPC) framework is designed for AUV, which improves the performance of trajectory tracking through online optimization. By incorporating salient features of Lyapunov-based nonlinear backstepping control, the contraction constraint is constructed to ensure the closed-loop stability. Within this framework, the actual limitation of executor saturation could be clearly considered. Next, the recursive feasibility and closed-loop stability of the LMPC-based control are rigorously proved. Also, the guaranteed region of attraction (ROA) is clearly described. Finally, the simulation results demonstrate the feasibility and robustness of the designed LMPC trajectory tracking method.