Open Access
Volume 39, Number 3, June 2021
Page(s) 477 - 483
Published online 09 August 2021
  1. Ridao P, Carreras M, Ribas D, et al. Intervention AUVs: the next challenge[J]. Annual Reviews in Control, 2015, 40: 227–241 10.1016/j.arcontrol.2015.09.015 [CrossRef] [Google Scholar]
  2. Shi Y, Shen C, Fang H, et al. Advanced control in marine mechatronic systems: a survey[J]. IEEE/ASME Trans on Mechatronics, 2017, 22(3): 1121–1131 10.1109/TMECH.2017.2660528 [CrossRef] [Google Scholar]
  3. Li Z, Liu W, Gao L, et al. Path planning method for AUV docking based on adaptive quantum-behaved particle swarm optimization[J]. IEEE Access, 2019, 7: 78665–78674 10.1109/ACCESS.2019.2922689 [CrossRef] [Google Scholar]
  4. Min J K, Woon-Kyung Baek, Kyoungnam Ha, et al. Way-point tracking for a hovering AUV by PID controller[C]//15th International Conference on Control, Automation and Systems, BEXCO, Busan, Korea, 2015 [Google Scholar]
  5. Zhang Lei. Research on fuzzy control of underwater vehicle path following based on genetic algorithm optimization[D]. Hangzhou: Zhejiang University, 2017(in Chinese) [Google Scholar]
  6. Wang Hongjian, Chen Ziyin, Jia Heming, et al. Three-dimensional path-following control of underactuated autonomous underwater vehicle with command filtered backstepping[J]. Acta Automatica Sinica, 2015, 41(3): 631–645 [Article] (in Chinese) [Google Scholar]
  7. Wang Jingqiang, Wang Cong, Wei Yingjie, et al. Position tracking control of autonomous underwater vehicles in the disturbance of unknown ocean currents[J]. Acta Armamentarii, 2019, 40(3): 583–591 10.3969/j.issn.1000-1093.2019.03.018 (in Chinese) [Google Scholar]
  8. Wang Jingqiang, Wang Cong, Wei Yingjie, et al. Path following of an underactuated AUV based on adaptive neural network backstepping sliding mode control[J]. Journal of Huazhong University of Science and Technology, 2019, 47(12): 12–17 [Article] (in Chinese) [Google Scholar]
  9. Shen C, Shi Y, Buckham B. Integrated path planning and tracking control of an AUV: a unified receding horizon optimization approach[J]. IEEE/ASME Trans on Mechatronics, 2017, 22(3): 1163–1173 10.1109/TMECH.2016.2612689 [CrossRef] [Google Scholar]
  10. Sun Y, Zhang C, Zhang G, et al. Three-dimensional path tracking control of autonomous underwater vehicle based on deep reinforcement learning[J]. Journal of Marine Science and Engineering, 2019, 7(12): 443 10.3390/jmse7120443 [CrossRef] [Google Scholar]
  11. Shi W, Song S, Wu C, et al. Multi pseudo q-learning-based deterministic policy gradient for tracking control of autonomous underwater vehicles[J]. IEEE Trans on Neural Networks and Learning Systems, 2019, 30(12): 3534–3546 10.1109/TNNLS.2018.2884797 [CrossRef] [Google Scholar]
  12. Yao Xuliang, Wang Xiaowei. Path following and obstacle avoidance control of AUV based on MPC guidance law[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(6): 1053–1062 [Article] (in Chinese) [Google Scholar]
  13. Yan Weisheng. Torpedo navigation mechanics[M]. Xi'an: Northwestern Polytechnical Press, 2005 (in Chinese) [Google Scholar]
  14. Sun Y, Cheng J, Zhang G, et al. Mapless motion planning system for an autonomous underwater vehicle using policy gradient-based deep reinforcement learning[J]. Journal of Intelligent & Robotic Systems, 2019, 96(3/4): 591–601 10.1007/s10846-019-01004-2 [CrossRef] [Google Scholar]
  15. Hagan M T, Demuth H B, Beale M H. Neural network design[M]. Beijing: China Machine Press, 2002 [Google Scholar]

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