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粒子群优化在直升机旋翼动平衡调整中的应用

刘红梅 吕琛 欧阳平超 王少萍

刘红梅, 吕琛, 欧阳平超, 等 . 粒子群优化在直升机旋翼动平衡调整中的应用[J]. 北京航空航天大学学报, 2011, 37(3): 283-288.
引用本文: 刘红梅, 吕琛, 欧阳平超, 等 . 粒子群优化在直升机旋翼动平衡调整中的应用[J]. 北京航空航天大学学报, 2011, 37(3): 283-288.
Liu Hongmei, Lü Chen, Ouyang Pingchao, et al. Helicopter rotor tuning based on neural network and particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3): 283-288. (in Chinese)
Citation: Liu Hongmei, Lü Chen, Ouyang Pingchao, et al. Helicopter rotor tuning based on neural network and particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(3): 283-288. (in Chinese)

粒子群优化在直升机旋翼动平衡调整中的应用

基金项目: 国家自然科学基金资助项目(61074083,50705005); 国防科技工业技术基础科研项目(Z132010B004)
详细信息
    作者简介:

    刘红梅(1978-),女,辽宁沈阳人,讲师,liuhongmei@buaa.edu.cn.

  • 中图分类号: V 267

Helicopter rotor tuning based on neural network and particle swarm optimization

  • 摘要: 传统的直升机旋翼调整方法没有考虑调整参数与振动信号之间的非线性关系,针对这一缺点,提出将广义回归神经网络(GRNN,General Regression Neural Network)和粒子群算法相结合的旋翼调整方法,采用GRNN网络建立旋翼动平衡调整模型,以桨叶的调整参数作为神经网络的输入,以旋翼转轴和机身的三向的加速度测量值作为网络输出,建立调整参数与直升机振动信号间的模型.以直升机振动作为目标函数,采用粒子群优化算法对桨叶的调整参数进行寻优,获得当直升机振动最小时的桨叶的调整量. 飞行实验结果表明,此方法可通过飞行测试获得的新数据对神经网络进行更新,使系统在使用过程中不断完善,并可在较少的飞行调整下完成旋翼的动平衡调整.

     

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出版历程
  • 收稿日期:  2010-01-11
  • 网络出版日期:  2011-03-31

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