以自主式水下航行器（AUV）的水动力参数辨识为背景，对其辨识方法进行研究。针对现阶段一些辨识方法中存在的辨识方程未采用AUV六自由度运动方程、需要数据消噪、对数据初值具有敏感性的问题，以及考虑AUV水动力参数多、参数之间耦合度高的特点，提出基于最小二乘准则和自适应粒子群优化算法的AUV水动力参数辨识方法，并利用仿真对该方法进行检验。仿真结果表明，该方法具有较好的可行性和鲁棒性，并同粒子群优化算法相比具有更好的稳定性和快速性。
Based on the hydrodynamic parameter identification of autonomous underwater vehicle (AUV), the identification method of the hydrodynamic parameter identification of autonomous underwater vehicle is studied. In view of the problems existing in some identification methods of autonomous underwater vehicle’s hydrodynamic parameter identification at present that the hydrodynamic parameter identification equation does not adopt AUV six degrees of freedom motion equation and the hydrodynamic parameter identification process of AUV requires noise elimination and some identification methods are sensitive to the initial value of data, and considering the characteristics that there are many AUV hydrodynamic parameters and the coupling between parameters is high, an AUV hydrodynamic parameter identification method based on least square criterion and adaptive particle swarm optimization algorithm is proposed, and the method is verified by simulation experiments. The simulation results show that the method is feasible and robust and has better stability and rapidity compared with the particle swarm optimization algorithm.
2021,43(11): 90-95 收稿日期：2020-10-20
DOI：10.3404/j.issn.1672-7649.2021.11.016
分类号：U674.941
作者简介：周怡(1996-)，男，硕士研究生，主要研究方向为自主式水下航行器
参考文献：
[1] 李庆梅, 刘平, 等. 基于NSGA-Ⅱ算法的水下机器人水平面运动参数辨识[J]. 上海第二工业大学学报, 2016, 33(2): 141–145
LI Qing-mei, LIU Ping, et al. Hydrodynamic coefficients identification for the horizontal plane motion of the underwater vehicle using NSGA-Ⅱ algorithm[J]. jouranl of shanghai polytechinc university, 2016, 33(2): 141–145
[2] 李迎华, 吴宝山, 张华. CFD动态网格技术在水下航行体非定常操纵运动预报中的应用研究[J]. 船舶力学, 2010, 14(10): 1100–1108
LI Ying-hua, WU Bao-shan, ZHANG Hua. Research on application of prediction for unsteady maneuvering motion of underwater vehicle by dynamic mesh technique in CFD[J]. Journal of Ship Mechanics, 2010, 14(10): 1100–1108
[3] KIM J Y, KIM K H, CHOI H S, et al. Estimation of hydrodynamic coefficients for an AUV using nonlinear observers[J]. IEEE Journal of Oceanic Engineering, 2002, 27(4): 830–840
[4] 陈玮琪, 颜开, 王宝寿. 辨识航行体水动力参数的智能方法[J]. 船舶力学, 2011, 15(4): 359–363
CHEN Wei-qi, YAN Kai, WANG Bao-shou. Intelligent identification of hydrodynamic parameters of navigating body[J]. Journal of Ship Mechanics, 2011, 15(4): 359–363
[5] RIDAO P, BATLLE J, CARRERAS M. Model identification of a low-speed UUV[C]//Proceedings of the 1st IFAC workshop on guidance and control of underwater vehicles, Glasgow, Scotland, UK: IFAC, 2003: 47−52.
[6] 林子淇. 水下机器人动力学建模与系统辨识技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2015.
LIN Ziqi. Research on dynamic modeling and techniques of system identification of underwater Vehicle[D]. Harbin: Harbin Engineering University, 2015.
[7] 王绍宾. 基于鲁棒控制的自主水下航行器控制方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
WANG Shaobin. Research on control method of autonomous underwater vehicle based on robust control[D]. Harbin: Harbin Engineering University, 2018.
[8] SILPA C. Autonomous underwater robot: vision and control.[D]. Australian National University, Australia, 2001.
[9] Kennedy J, Eberhart R. Particle swarm optimization[C]//IEEE Int’l Confon Neural Networks. Perth, Australia, 1995: 1942−1948.
[10] SHI Y H, EBERHART R C. A modified particle swarm optimizer[J]. IEEE international conference on evolutionary computation, anchorage alsaka, 1998: 69–73
[11] 沈智鹏, 代昌盛. 欠驱动船舶路径跟踪的强化学习迭代滑模控制[J]. 哈尔滨工程大学学报, 2017(5).
SHEN Zhipeng, DAI Changsheng. Iterative sliding mode control based on reinforced learning and used for path tracking of under-actuated ship[J]. Journal of Harbin Engineering University[J], 2017(5).
[12] 罗彩君. 基于粒子群算法的欠驱动船舶航向控制研究[J]. 舰船科学技术, 2019, 41(10): 25–27
LUO Cai-jun. Research on underactuated ship heading control based on particle swarm optimization[J]. ship science and technology, 2019, 41(10): 25–27
[13] 韦波. 粒子群优化的RBF神经网络在海上运输事故预测中的应用[J]. 舰船科学技术, 2016, 38(4): 181–183
WEI Bo. Application of RBF neural network in the prediction of sea transportation accidents in the particle swarm optimization[J]. ship science and technology, 2016, 38(4): 181–183
[14] 方淡玉, 黄橙, 等. 基于船舶六自由度运动的船舶航行安全实时监控系统研究[J]. 数字技术与应用, 2013(6).
FANG Dan-yu, HUANG Cheng, et al. Research on real-time monitoring system of ship navigation safety based on ship's six degrees of freedom movement[J]. Digital Technology and Application, 2013(6).
