留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于SVM辨识的涡扇发动机全包线稳态控制方法

刘建勋 李罡 吕孟军 尉询凯

刘建勋, 李罡, 吕孟军, 尉询凯. 基于SVM辨识的涡扇发动机全包线稳态控制方法[J]. 航空动力学报, 2012, 27(7): 1613-1618.
引用本文: 刘建勋, 李罡, 吕孟军, 尉询凯. 基于SVM辨识的涡扇发动机全包线稳态控制方法[J]. 航空动力学报, 2012, 27(7): 1613-1618.
LIU Jian-xun, LI Gang, LV Meng-jun, WEI Xun-kai. Identification method based on support vector machines for turbofan engine steady state control in full flight envelop and various working conditions[J]. Journal of Aerospace Power, 2012, 27(7): 1613-1618.
Citation: LIU Jian-xun, LI Gang, LV Meng-jun, WEI Xun-kai. Identification method based on support vector machines for turbofan engine steady state control in full flight envelop and various working conditions[J]. Journal of Aerospace Power, 2012, 27(7): 1613-1618.

基于SVM辨识的涡扇发动机全包线稳态控制方法

Identification method based on support vector machines for turbofan engine steady state control in full flight envelop and various working conditions

  • 摘要: 针对涡扇发动机全飞行包线范围稳态最优控制器的设计问题,首先根据不同飞行条件下发动机各工作状态的稳态“小偏差”线性模型,采用线性二次型调节器(LQR)分别设计得到相应的发动机最优线性控制器参数,然后将所得到的线性控制器用支持向量机方法进行非线性逼近,得到控制器参数的支持向量机辨识模型,以满足发动机全包线、全状态稳态控制的需要.支持向量机模型的输入为飞行高度、马赫数和稳态转速,输出为线性控制器参数.应用实例表明:该方法在全包线范围内对发动机最优稳态控制器的逼近误差均在2%以内,能较好满足控制精度要求.

     

  • [1] Wiseman M W.Advanced online control mode selection for gas turbine aircraft engines .Cincinnati,Ohio:University of Cincinnati,1995.
    [2] 樊思齐.航空发动机控制(下册)[M].西安:西北工业大学出版社,2008.
    [3] 赵海.改进型LQR方法在飞机控制律设计中的应用[J].飞机工程,2006,3(1):63-66. ZHAO Hai.Application of improved LQR method to control law design of an airplane[J].Aircraft Engineering,2006,3(1):63-66.(in Chinese)
    [4] 王前宇,孙健国,李秋红.航空发动机三变量鲁棒控制器设计[J].航空动力学报,2009,24(11):2607-2611. WANG Qianyu,SUN Jianguo,LI Qiuhong.Three variables robust controller design for aeroengine[J].Journal of Aerospace Power,2009,24(11):2607-2611.(in Chinese)
    [5] 刘建勋,李应红,陈永刚.航空发动机LQR控制的模糊神经网络方法[J].航空动力学报,2004,19(6):838-843. LIU Jianxun,LI Yinghong,CHEN Yonggang.Aeroengine LQR control based on fuzzy-neural network[J].Journal of Aerospace Power,2004,19(6):838-843.(in Chinese)
    [6] 郭迎清,章泓,杨云波.利用神经网络设计航空发动机全包线最优控制器[J].航空动力学报,2000,15(3):331-333. GUO Yingqing,ZHANG Hong,YANG Yunbo.The design of aero-engine optimal controller suitable for all flight envelope with neural network approximator[J].Journal of Aerospace Power,2000,15(3):331-333.(in Chinese)
    [7] 梁华,李应红,尉询楷.航空发动机的支持向量机自适应PID控制[J].航空动力学报,2007,22(1):137-141. LIANG Hua,LI Yinghong,WEI Xunkai.Support vector machines PID adaptive control in aeroengine[J].Journal of Aerospace Power,2007,22(1):137-141.(in Chinese)
    [8] 袁胜发,褚福磊.基于改进型球结构支持向量机的故障诊断方法及其应用[J].推进技术,2006,27(1):1-4. YUAN Shengfa,CHU Fulei.Fault diagnosis based on improved sphere support vector machines[J].Journal of Propulsion Technology,2006,27(1):1-4.(in Chinese)
    [9] 刘建勋.涡扇发动机起动过程的学习机模型与动态控制研究 .西安:空军工程大学,2005. LIU Jianxun.Mathematical model of turbofan engine starting based on learning machines and dynamical control research .Xi'an: Airforce Engineering University,2005. (in Chinese)
    [10] WEI Xunkai,LI Yinghong.Comparative study of extreme learning machine and support vector machine[J].LNCS,2006(3971):1089-1095.
    [11] Smola A J,Schölkopf B.A tutorial on support vector regression .Royal Holloway College,University of London,Technical Report NC-TR-98-030,1998.
    [12] Vapnik V N,Chapelle O.Choosing multiple parameters for support vector machines[J].Machine Learning,2002,46(1):124-136.
    [13] Schölkopf B,Smola A J.New support vector algorithms[J].Neural Computation,2000,12(5):1207-1245.
    [14] 吕建伟,李军.考虑雷诺数、湿度等影响因素的涡扇发动机小偏差模型[J].航空动力学报,2005,20(1):44-48. LV Jianwei,LI Jun.Reynolds number and humidity effect on the small deviation model of turbofan engine[J].Journal of Aerospace Power,2005,20(1):44-48.(in Chinese)
    [15] 杨刚,姚华.一种航空发动机相似多变量控制方法[J].航空动力学报,2006,21(3):588-594. YANG Gang,YAO Hua.Theory of similarity based aeroengine multivariable control method[J]. Journal of Aerospace Powe,2006,21(3):588-594.(in Chinese)
  • 加载中
计量
  • 文章访问数:  1519
  • HTML浏览量:  2
  • PDF量:  552
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-07-19
  • 刊出日期:  2012-07-28

目录

    /

    返回文章
    返回