• CN:11-2187/TH
  • ISSN:0577-6686

›› 2010, Vol. 46 ›› Issue (11): 96-100.

• 论文 • 上一篇    下一篇

基于遗传算法的自治水下机器人水动力参数辨识方法

袁伟杰;刘贵杰;朱绍锋   

  1. 中国海洋大学工程学院;上海交通大学海洋工程国家重点实验室
  • 发布日期:2010-06-05

Identification Method of Hydrodynamic Parameters of Autonomous Underwater Vehicle Based on Genetic Algorithm

YUAN Weijie;LIU Guijie;ZHU Shaofeng   

  1. College of Engineering, Ocean University of China The State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University
  • Published:2010-06-05

摘要: 针对传统确定自治水下机器人(Autonomous underwater vehicle, AUV)水动力参数试验和理论计算的困难性及以往辨识方法优化结果趋于局部最优解的缺点,提出一种基于遗传算法的AUV水动力参数辨识方法,该方法不受参数初值选取的影响,具有较好的鲁棒性和全局寻优特性,可为AUV运动控制、状态预报及控制系统开发等提供动力学模型。针对开架式AUV原型样机CRanger-01,在对其进行动力学建模分析的基础上,利用该方法对其水平面运动水动力参数进行辨识,得出了CRanger-01的19个水平面水动力参数,与实际值进行比较后,辨识误差在允许范围之内。通过运动仿真对模型进行验证,结果表明该算法能有效辨识AUV水动力参数,可为工程实践提供参考依据。

关键词: 参数辨识, 水动力参数, 遗传算法, 自治水下机器人

Abstract: With a view to the difficulty of the traditional hydrodynamic parameters experiments and theoretical calculation methods for autonomous underwater vehicle (AUV) and the drawback of optimum results tending to local optimization of the ever identification methods, a genetic algorithm based hydrodynamic parameters identification method of AUV is presented. This method has better robustness and can find the globally optimal point without initial values of parameters. It can provide a dynamic model for motion control, condition prediction and control system exploitation of AUV. Aimed at an open-frame AUV prototype named by CRanger-01, the genetic algorithm is used to identify the horizontal plane hydrodynamic parameters based on the analysis of the dynamic modeling. Then nineteen horizontal plane hydrodynamic parameters of CRanger-01 are identified, compared with the actual values, the identification errors are in the allowable range. By the simulation of the dynamic model, it demonstrates that the method can identify the hydrodynamic parameters of AUV effectively and can provide a reference for engineering practice.

Key words: Autonomous underwater vehicle, Genetic algorithm, Hydrodynamic parameters, Parameter identification

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