Research of hydrodynamic parameter identification for underwater vehicle using swarm intelligence algorithm

被引：0
|
作者：
Chen, Wei-Qi [1 ,2 ]
Yan, Kai [2 ]
Shi, Gan-Jun [2 ]
Wang, Shi-Tong [1 ]
Liu, Zhi-Yong [2 ]
机构：
[1] Southern Yangtze University, Wuxi 214026, China
[2] China Ship Scientific Research Center, Wuxi 214082, China
来源：
Chuan Bo Li Xue/Journal of Ship Mechanics | 2007年 / 11卷 / 01期
关键词：
Algorithms - Autonomous underwater vehicles - Computer simulation - Differential equations - Experiments - Functions - Matrix algebra - Nonlinear equations;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Based on underwater vehicle's nonlinear differential equation and observation equations, swarm intelligence algorithm is used to identify ten hydrodynamic parameters from simulation observed data of the motions of underwater vehicle. Experimental results show that this method is highly effective and efficient, and moreover it has no requirements on the differentiability and continuity of the objective function, and consequently does not need to perform complex matrix calculations, so this method is suitable, for applications in identification of nonlinear hydrodynamics of complex system.
引用
收藏
页码：40 / 46
相关论文
共 50 条
  • [31] Effect of the damping and excitation on the identification of the hydrodynamic parameters for an underwater robotic vehicle
    Mahfouz, AB
    Haddara, MR
    OCEAN ENGINEERING, 2003, 30 (08) : 1005 - 1025
  • [32] Research on the Hydrodynamic Calculation of Variable Structure Underwater Vehicle Based on CFD
    Liu, Xiaomeng
    Zhang, Qifeng
    Tian, Qiyan
    Wang, Yaxing
    Yang, Xuejiao
    Li, Dehao
    Wang, Xiaohui
    INTELLIGENT ROBOTICS AND APPLICATIONS (ICIRA 2022), PT III, 2022, 13457 : 268 - 278
  • [33] Research on Hydrodynamic Parameters Identification of Underwater Vehicle Glide Motion with Controllable-wings Based on Trial Data
    Zhang, Chao
    Zhang, Hua
    Xiao, Donglin
    Zheng, Peng
    Wang, Jian
    Cao, Yuanshan
    Ship Building of China, 2023, 64 (06) : 272 - 283
  • [34] Accelerating Underwater Vehicle Hydrodynamic Analysis Using Convolutional Networks
    Chen, Qian
    Zeng, Zheng
    Lian, Lian
    2018 OCEANS - MTS/IEEE KOBE TECHNO-OCEANS (OTO), 2018,
  • [35] Identification of an Autonomous Underwater Vehicle Hydrodynamic Model Using the Extended, Cubature, and Transformed Unscented Kalman Filter
    Sabet, Mohammad Taghi
    Daniali, Hamidreza Mohammadi
    Fathi, Alireza
    Alizadeh, Ebrahim
    IEEE JOURNAL OF OCEANIC ENGINEERING, 2018, 43 (02) : 457 - 467
  • [36] Parameter Identification of a Multilayer Perceptron Neural Network using an Optimized Salp Swarm Algorithm
    Al-Laham, Mohamad
    Abdullah, Salwani
    Al-Ma'aitah, Mohammad Atwah
    Al-Betar, Mohammed Azmi
    Kassaymeh, Sofian
    Azzazi, Ahmad
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (06) : 1221 - 1232
  • [37] Precise parameter identification of a PEMFC model using a robust enhanced salp swarm algorithm
    Saidi, Salem
    Marrouchi, Sahbi
    Alhasnawi, Bilal Naji
    Pathak, Pawan Kumar
    Alshammari, Obaid
    Albaker, Abdullah
    Abbassi, Rabeh
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2024, 71 : 937 - 951
  • [38] Research on the Methods of Underwater Vehicle Noise Source Identification
    Wang Qian
    Hou Hong
    Wang Hongwei
    Zhang Zhimin
    2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2015, : 269 - 272
  • [39] A Hybrid Swarm Intelligence Algorithm for Vehicle Routing Problem With Time Windows
    Shen, Yang
    Liu, Mingde
    Yang, Jian
    Shi, Yuhui
    Middendorf, Martin
    IEEE ACCESS, 2020, 8 : 93882 - 93893
  • [40] UNDERWATER VEHICLE SYSTEM IDENTIFICATION USING LASER LIGHT, DECOUPLED DYNAMICS AND EVOLUTIONARY ALGORITHM
    Chou, Yu-Cheng
    INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, 2015, VOL 9, 2016,
12345