Improved Model Predictive Control With Dung Beetle Optimizer for Depth Tracking of Autonomous Underwater Vehicles

This paper presents an improved model predictive control (MPC) method based on the dung beetle optimizer (DBO) for achieving precise depth control of autonomous underwater vehicles (AUVs). Initially, the concept of depth tracking is introduced, followed by the adoption of the DBO‐MPC approach to facilitate depth tracking control of the AUV. The DBO algorithm is employed to optimize the objective function, enabling optimal control of the AUV's speed and attitude while satisfying control constraints. Three‐dimensional simulation results demonstrate that the DBO‐MPC method outperforms both the classical backstepping control method and the heuristic Particle swarm optimization‐MPC (PSO‐MPC) control method in addressing the depth tracking problem. The algorithm effectively resolves the issue of velocity jumps, thereby enhancing the accuracy of motion control. This study underscores the effectiveness and feasibility of the proposed DBO‐MPC technique for depth tracking in AUVs, showcasing its potential for real‐world applications.