A hybrid polynomial-based optimization method for underwater gliders with parameter uncertainty

被引:19
作者
Wu, Hongyu [1 ]
Niu, Wendong [2 ]
Zhang, Yuling [1 ]
Wang, Shuxin [2 ]
Yan, Shaoze [1 ]
机构
[1] Tsinghua Univ, Dept Mech Engn, State Key Lab Tribol Adv Equipment, Beijing 100084, Peoples R China
[2] Tianjin Univ, Key Lab Mech Theory & Equipment Design, Minist Educ, Tianjin 300072, Peoples R China
基金
中国国家自然科学基金;
关键词
Hybrid polynomial; Multidisciplinary optimization; Underwater glider; Dynamic analysis; Uncertainty analysis; DIFFERENTIAL EVOLUTION; SHAPE OPTIMIZATION; STABILITY; STRATEGY;
D O I
10.1016/j.apor.2023.103486
中图分类号
P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
As a type of enduring platform for ocean exploration, underwater glider usually contains some uncertain parameters in actual engineering applications, which are related to manufacturing errors, assembly errors, and sensor measurement errors. Uncertain parameters will make the glider performance show uncertain characteristics, so it is necessary to consider their effects in the design optimization process for an underwater glider. In this paper, a hybrid polynomial-based optimization method is proposed to solve optimization problems of underwater gliders with parameter uncertainty. The hybrid polynomial consists of power function terms and trigonometric function terms, and it is used for fitting surrogate models of the glider dynamic models. The fitted surrogate models are used for quickly calculating the performance evaluation parameters of underwater gliders. Specifically, power function terms and trigonometric function terms correspond to optimization design variables and uncertain parameters, respectively. The hybrid polynomial method covers the convenience of the traditional response surface method and the advantage of the Chebyshev polynomial in interval analysis. Based on surrogate models and interval analysis, we can estimate boundary functions of performance evaluation parameters, and they are employed to construct optimization objective functions. Then, the optimization iteration calculation can be carried out. Here, the proposed method is used for determining the optimal control parameter values of the Petrel-II glider with the uncertainty of some configuration and measurement parameters. The optimization objectives are to minimize the specific energy consumption and maximize the average advance velocity simultaneously. Finally, some laws of the glider performance are summarized.
引用
收藏
页数:15
相关论文
共 49 条
  • [1] [Anonymous], 2001, Intelligent optimization algorithm and its application
  • [2] A Monte Carlo simulation based inverse propagation method for stochastic model updating
    Bao, Nuo
    Wang, Chunjie
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2015, 60-61 : 928 - 944
  • [3] Nonlinear gliding stability and control for vehicles with hydrodynamic forcing
    Bhatta, Pradeep
    Leonard, Naomi Ehrich
    [J]. AUTOMATICA, 2008, 44 (05) : 1240 - 1250
  • [4] Exploring Ocean Biogeochemistry Using a Lab-on-Chip Phosphate Analyser on an Underwater Glider
    Birchill, Antony J.
    Beaton, A. D.
    Hull, Tom
    Kaiser, Jan
    Mowlem, Matt
    Pascal, R.
    Schaap, A.
    Voynova, Yoana G.
    Williams, C.
    Palmer, M.
    [J]. FRONTIERS IN MARINE SCIENCE, 2021, 8
  • [5] Seaglider: A long-range autonomous underwater vehicle for oceanographic research
    Eriksen, CC
    Osse, TJ
    Light, RD
    Wen, T
    Lehman, TW
    Sabin, PL
    Ballard, JW
    Chiodi, AM
    [J]. IEEE JOURNAL OF OCEANIC ENGINEERING, 2001, 26 (04) : 424 - 436
  • [6] Dynamics of underwater gliders in currents
    Fan, Shuangshuang
    Woolsey, Craig A.
    [J]. OCEAN ENGINEERING, 2014, 84 : 249 - 258
  • [7] Fossen T.I., 2011, Handbook of Marine Craft Hydrodynamics and Motion Control, DOI [10.1002/9781119994138, DOI 10.1002/9781119994138]
  • [8] Fossen T.I., 1994, Guidance and Control of Ocean Vehicles
  • [9] Multi-objective shape optimization of autonomous underwater glider based on fast elitist non-dominated sorting genetic algorithm
    Fu, Xiaoyun
    Lei, Lei
    Yang, Gang
    Li, Baoren
    [J]. OCEAN ENGINEERING, 2018, 157 : 339 - 349
  • [10] Dynamic modeling and experimental analysis of an underwater glider in the ocean
    Jing, Guo
    Lei, Lei
    Gang, Yang
    [J]. APPLIED MATHEMATICAL MODELLING, 2022, 108 : 392 - 407