Prescribed Performance Lyapunov‐Based Model Predictive Tracking Control for Autonomous Underwater Vehicles Subject to Disturbances: A Dynamically Weighted Approach

Traditional Lyapunov‐based model predictive tracking control methods can handle input constraints, but ensuring state constraints and transient tracking performance of autonomous underwater vehicles (AUVs) under complex ocean disturbances remains challenging. To overcome the challenge, a novel prescribed‐performance Lyapunov‐based model predictive control (LMPC) scheme is proposed in this article. It integrates a dynamically weighted approach to online optimize the prescribed performance LMPC weights, thereby further improving tracking accuracy and adaptability. Additionally, a Gaussian distribution‐based dynamic weighting strategy is proposed, in which the parameters of the Gaussian function that generate the LMPC weights are optimized online using a genetic algorithm. This transforms the weight tuning problem from an unstructured search in the solution space into a structured search within predefined high‐probability regions, thereby improving the stability of the search. Furthermore, the robust contraction constraint established by the prescribed performance auxiliary control laws ensures closed‐loop stability, and theoretical analyses evaluate recursive feasibility. Finally, simulations and comparisons are conducted to validate the effectiveness and superiority of the proposed scheme.