GONG J B, WANG P J, WANG H, et al. Intelligent fuzzy inference method for generating UUV hull parameters[J]. Chinese Journal of Ship Research, 2024, 19(6): 56–63 (in both Chinese and English). DOI: 10.19693/j.issn.1673-3185.04063
Citation: GONG J B, WANG P J, WANG H, et al. Intelligent fuzzy inference method for generating UUV hull parameters[J]. Chinese Journal of Ship Research, 2024, 19(6): 56–63 (in both Chinese and English). DOI: 10.19693/j.issn.1673-3185.04063

Intelligent fuzzy inference method for generating UUV hull parameters

More Information
  • Received Date: July 13, 2023
  • Revised Date: August 28, 2024
  • Accepted Date: September 13, 2024
  • Available Online: September 03, 2024
  • Published Date: October 11, 2024
© 2024 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Objective 

    This study proposes a strategy algorithm based on the fuzzy inference method for the rapid optimization of unmanned underwater vehicle (UUV) hull design parameters.

    Method 

    The initial solutions generated by the genetic algorithm are first fuzzified during the fuzzification stage, then used as training samples, and the antecedent parameters of the fuzzy rules are obtained using an equal interval fuzzy partition strategy with the membership values of all calculated UUV solutions. Next, a least learning machine (LLM) is employed to solve the consequent parameters of the fuzzy rules. Based on the generated antecedent and consequent parameters, new UUV solutions are created and the evaluation membership values for speed and range are calculated. Finally, these new UUV solutions are tested against the constraint conditions to obtain optimized and compliant UUV design parameters.

    Results 

    The experimental results show that within 20 seconds, the intelligent fuzzy inference method can infer multiple UUV hull parameter schemes with a combined evaluation membership degree score for speed and range of over 170 points based on the initial UUV hull parameters generated by genetic algorithms.

    Conclusion 

    The proposed method effectively enhances design efficiency and balances speed and range. The findings of this study can provide valuable references for the intelligent and rapid generation of UUV hull parameters.

  • [1]
    楚立鹏, 鄢宏华, 范强, 等. 国外水下无人潜航器及其通信技术发展综述[J]. 中国电子科学研究院学报, 2022, 17(2): 112–118. doi: 10.3969/j.issn.1673-5692.2022.02.002

    CHU L P, YAN H H, FAN Q, et al. Overview of unmanned underwater vehicles and the communication technologies abroad[J]. Journal of China Academy of Electronics and Information Technology, 2022, 17(2): 112–118 (in Chinese). doi: 10.3969/j.issn.1673-5692.2022.02.002
    [2]
    邱志明, 马焱, 孟祥尧, 等. 水下无人装备前沿发展趋势与关键技术分析[J]. 水下无人系统学报, 2023, 31(1): 1–9.

    QIU Z M, MA Y, MENG X Y, et al. Analysis on the development trend and key technologies of unmanned underwater equipment[J]. Journal of Unmanned Undersea Systems, 2023, 31(1): 1–9 (in Chinese).
    [3]
    张宇新, 李鹏, 魏博, 等. 水下航行器阻力计算及结构设计[J]. 应用科技, 2023, 50(5): 141–148.

    ZHANG Y X, LI P, WEI B, et al. Resistance calculation and structure design of underwater vehicles[J]. Applied Science and Technology, 2023, 50(5): 141–148 (in Chinese).
    [4]
    姜宜辰, 赵月, 熊济时, 等. 水下航行器艇体形状对阻力及流噪声综合影响[J]. 哈尔滨工程大学学报, 2022, 43(1): 76–82,138.

    JIANG Y C, ZHAO Y, XIONG J S, et al. Comprehensive influence of underwater vehicle hull shape on resistance and flow noise[J]. Journal of Harbin Engineering University, 2022, 43(1): 76–82,138 (in Chinese).
    [5]
    陈力铭, 邱浩波, 高亮. 基于梯度增强Kriging方法的水下航行器结构优化设计[J]. 中国舰船研究, 2021, 16(4): 79–85, doi: 10.19693/j.issn.1673-3185.02066

    CHEN L M, QIU H B, GAO L. Structural design optimization of underwater vehicle via gradient-enhanced Kriging[J]. Chinese Journal of Ship Research, 2021, 16(4): 79–85 (in both Chinese and English doi: 10.19693/j.issn.1673-3185.02066
    [6]
    IGNACIO L C, VICTOR R R, FRANCISCO D R R, et al. Optimized design of an autonomous underwater vehicle, for exploration in the Caribbean Sea[J]. Ocean Engineering, 2019, 187: 106184. doi: 10.1016/j.oceaneng.2019.106184
    [7]
    HOU S P, ZHANG Z J, LIAN H T, et al. Hull shape optimization of small underwater vehicle based on Kriging-based response surface method and multi-objective optimization algorithm[J]. Brodogradnja, 2022, 73(3): 111–134. doi: 10.21278/brod73307
    [8]
    SAGHAFI M, LAVIMI R. Optimal design of nose and tail of an autonomous underwater vehicle hull to reduce drag force using numerical simulation[J]. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 2020, 234(1): 76–88. doi: 10.1177/1475090219863191
    [9]
    ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338–353. doi: 10.1016/S0019-9958(65)90241-X
    [10]
    MAMDANI. Application of fuzzy logic to approximate reasoning using linguistic synthesis[J]. IEEE Transactions on Computers, 1977, C-26(12): 1182–1191. doi: 10.1109/TC.1977.1674779
    [11]
    李国勇, 杨丽娟. 神经·模糊·预测控制及其 MATLAB 实现[M]. 4版. 北京: 电子工业出版社, 2018: 262-263.

    LI G Y, YANG L J. Neural, Fuzzy, and predictive control and its MATLAB implementation[M]. 4th ed. Beijing: Electronic Industry Press, 2018: 262-263 (in Chinese).
    [12]
    梁霄, 张均东, 李巍, 等. 水下机器人T-S型模糊神经网络控制[J]. 电机与控制学报, 2010, 14(7): 99–104, doi: 10.15938/j.emc.2010.07.015

    LIANG X, ZHANG J D, LI W, et al. T-S fuzzy neural network control for autonomous underwater vehicles[J]. Electric Machines and Control, 2010, 14(7): 99–104(in Chinese) doi: 10.15938/j.emc.2010.07.015
    [13]
    钱缘. 基于T-S模糊模型的UUV鲁棒运动控制[D]. 上海: 上海交通大学, 2020.

    QIAN Y. Robust motion control of unmanned underwater vehicles based on T-S fuzzy model[D]. Shanghai: Shanghai Jiao Tong University, 2020 (in Chinese).
    [14]
    MYRING D F. A theoretical study of body drag in subcritical axisymmetric flow[J]. Aeronautical Quarterly, 1976, 27(3): 186–194. doi: 10.1017/S000192590000768X
    [15]
    李阳. 螺旋推进式水下航行器结构设计与外形优化[D]. 青岛: 青岛科技大学, 2020.

    LI Y. Structural design and shape optimization of spiral propulsion underwater vehicle[D]. Qingdao: Qingdao University of Science and Technology, 2020 (in Chinese).
    [16]
    GILLMER T, JOHNSON B. Introduction to naval architecture[M]. Annapolis, MD, USA: Naval Institute Press, 1982.
    [17]
    ALAM K, RAY T, ANAVATTI S G. Design optimization of an unmanned underwater vehicle using low- and high-fidelity models[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 47(11): 2794–2808.
    [18]
    黄文飞, 张驰, 陈友鹏, 等. 水下无人航行器发电及储能技术研究[J]. 舰船科学技术, 2024, 46(1): 115–120.

    HUANG W F, ZHANG C, CHEN Y P, et al. Reviews of power generation and energy storage technology for unmanned underwater vehicles[J]. Ship Science and Technology, 2024, 46(1): 115–120 (in Chinese).
    [19]
    ZHOU T, CHUNG F L, WANG S T. Deep TSK fuzzy classifier with stacked generalization and triplely concise interpretability guarantee for large data[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(5): 1207–1221. doi: 10.1109/TFUZZ.2016.2604003
    [20]
    DENG Z H, CHOI K S, JIANG Y, et al. Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods[J]. IEEE Transactions on Cybernetics, 2014, 44(12): 2585–2599. doi: 10.1109/TCYB.2014.2311014
    [21]
    DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197. doi: 10.1109/4235.996017
    [22]
    TAKAGI T, SUGENO M. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1985, SMC-15(1): 116–132. doi: 10.1109/TSMC.1985.6313399
    [23]
    HUANG G B, SIEW S K. Extreme learning machine: RBF network case[C]//Proceedings of the ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004. Kunming: IEEE, 2012: 1029-1036.
    [24]
    Office of the Chief of Naval Operations. The navy unmanned undersea vehicle (UUV) master plan[R]. Washington D.C: Office of the Chief of Naval Operations, 2004.
    [25]
    周念福, 邢福, 渠继东. 大排量无人潜航器发展及关键技术[J]. 舰船科学技术, 2020, 42(7): 1–6.

    ZHOU N F, XING F, QU J D. The development of large displacement unmanned underwater vehicle of foreign navy and its key technologies[J]. Ship Science and Technology, 2020, 42(7): 1–6 (in Chinese).

Catalog

    Article views (780) PDF downloads (75) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return