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Model parameters estimation of aero-engine based on hybrid optimization algorithm

LIQiu-hong

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文章导航 > 航空动力学报  > 2011 >  26(7): 1665-1671
LIQiu-hong. Model parameters estimation of aero-engine based on hybrid optimization algorithm[J]. 航空动力学报, 2011, 26(7): 1665-1671.
引用本文: LIQiu-hong. Model parameters estimation of aero-engine based on hybrid optimization algorithm[J]. 航空动力学报, 2011, 26(7): 1665-1671.
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LI Qiu-hong. Model parameters estimation of aero-engine based on hybrid optimization algorithm[J]. Journal of Aerospace Power, 2011, 26(7): 1665-1671.
Citation: LI Qiu-hong. Model parameters estimation of aero-engine based on hybrid optimization algorithm[J]. Journal of Aerospace Power, 2011, 26(7): 1665-1671.
shu

Model parameters estimation of aero-engine based on hybrid optimization algorithm

Model parameters estimation of aero-engine based on hybrid optimization algorithm

    摘要
  • 摘要: A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization (PSO) and least squares optimization (LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model (SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.

     

    Abstract: A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization (PSO) and least squares optimization (LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model (SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.

     

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    • 参考文献(13)
  • [1] ZHAO Jiaqing,WANG Ling.Center based genetic algorithm and its application to the stiffness equivalence of the aircraft wing[J].Expert Systems with Applications,2011,38(5):6254-6261.
    [2] YANG Jingan,ZHUANG Yanbin.An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem[J].Applied Soft Computing,2010,10(2):653-660.
    [3] MA Hai,WANG Yanjiang.An artificial fish swarm algorithm based on chaos search //The 5th International Conference on Natural Computation (ICNC'09).Tianjin:IEEE,2009:118-121.
    [4] Rahimi-Vahed A,Mirzaei A H.A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem[J].Computers & Industrial Engineering,2007,53(4):642-666.
    [5] Mohammadi A,Ziarati K.A powerful bee swarm optimization algorithm //The 13th IEEE International Multitopic Conference.Islamabad:IEEE,2009:1-6.
    [6] ZHAN Zhihui,ZHANG Jun,LI Yun,et al.Adaptive particle swarm optimization[J].Systems,Man,and Cybernetics,Part B:Cybernetics,IEEE Transactions on,2009,39(6):1362-1381.
    [7] Ling S H,Iu H,Leung F H F,et al.Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging[J].Industrial Electronics,IEEE Transactions on,2008,55(9):3447-3459.
    [8] Sentinella M R,Casalino L.Hybrid evolutionary algorithm for the optimization of interplanetary trajectories[J].Journal of Spacecraft and Rockets,2009,46(2):365-372.
    [9] Sugiyama N.Derivation of ABCD system matrices from nonlinear dynamic simulation of jet engines .AIAA-1992-3319,1992.
    [10] FENG Zhengping,SUN Jianguo.A new method for establishing a state space model of aeroengine[J].Journal of Aerospace Power,1998,13(4):435-438.(in Chinese)
    [11] FENG Zhengping,SUN Jianguo.Modeling of small perturbation state variable model for aeroengines[J].Journal of Propulsion Technology,2001,22(1):54-57.(in Chinese)
    [12] YUAN Chunfei,YAO Hua,YANG Gang.On-board real-time adaptive model of aero-engine[J].Chinese Journal of Aeronautics,2006,27(4):561-564.(in Chinese)
    [13] LI Qiuhong,SUN Jianguo.Aero-engine state variable modeling based on the genetic algorithm[J].Journal of Aerospace Power,2006,21(2):427-431.(in Chinese)
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出版历程
  • 收稿日期:  2010-06-29
  • 修回日期:  2011-03-02
  • 刊出日期:  2011-07-28

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