Utilizing Lyapunov-Based Model Predictive Control to Achieve Dynamic Positioning for the Haizhe Autonomous Underwater Vehicle

This paper demonstrates the application of a Lyapunov-Based Model Predictive Control (LBMPC) framework in achieving dynamic positioning (DP) using the Haizhe autonomous underwater vehicle (AUV) as a case study. Moreover, addressing the thrust allocation (TA) subproblem and optimizing DP control, LBMPC considers real-world restrictions. It makes use of a Proportional-Integral-Derivative (PID) controller, which ensures stability in the closed-loop system and continual feasibility within the system. Furthermore, attaining an extensive region of attraction, the contraction constraint improves stability. Control theory and optimization techniques are integrated into the framework, enabling smooth online optimization integration for enhanced control performance. The success of this strategy is shown by simulation findings on the Haizhe AUV, which represents a breakthrough in underwater vehicle control.