Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

• Published in: Ocean engineering 2024-10, Vol.310, p.118805, Article 118805
• Main Authors: Li, Jiawei, Xia, Yingkai, Xu, Guohua, Guo, Zhengjiang, Han, Hao, Wu, Zhe, Xu, Kan
• Format: Article
• Language: English
• Subjects: AUV; Enhanced three-dimensional tracking control; FMPC; FTTSMC; Variable condition tracking control
• ISSN: 0029-8018
• DOI: 10.1016/j.oceaneng.2024.118805

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness. •A novel FMPC-FTTSMC method is proposed to enhance three-dimensional trajectory tracking for AUVs in variable conditions.•A fuzzy weight allocator is proposed to address changing objectives, dynamically adjusting control strategies.•Switching variables are employed to facilitate the transition between soft and hard constraints within the cost function.•The proposed control architecture offers reduced computation time compared to traditional model predictive control.