Formation Control of Autonomous Underwater Vehicles With Unknown Absolute Position Using Rigid Graph-Based MPC

Formation control of autonomous underwater vehicles (AUVs) is of great significance to the Internet of Underwater Things (IoUT). Currently, various methods are employed for AUV formation control. Among them, rigid graph-based approaches, which rely solely on relative information between AUVs, are particularly suitable for underwater environments. However, traditional rigid graph-based formation control methods often face challenges, such as control jumps and weak robustness in dynamic underwater environments. To address these challenges, a novel rigid graph-based model predictive controller (RGMPC) is proposed in this article. The proposed approach integrates model predictive control with traditional rigid graph-based methods, effectively avoiding issues, such as control jumps and thrust saturation while enhancing robustness. Moreover, constraints are constructed using backstepping methods to theoretically ensure the closed-loop stability of the algorithm. Finally, the algorithm is validated through extensive simulations based on mathematical models and further tested in the Gazebo simulator, fully demonstrating its feasibility and effectiveness.