蚁群初始化小波网络的汽油机油膜参数辨识研究
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摘要
建立瞬态工况小波网络发动机油膜模型,利用蚁群算法对小波网络参数进行初始化寻优,将其作为小波网络参数初始值,以提高小波网络的训练速度和误差精度,并基于该网络模型测试了发动机空燃比瞬态过程,然后利用瞬态工况试验数据进行了仿真,并与台架试验实际数据进行对比。结果表明,基于蚁群算法初始化小波网络模型能有效地辨识发动机瞬态工况油膜参数,高精度地逼近空燃比瞬态过程,不仅具有较强的泛化能力,而且大大缩短了训练时间。蚁群初始化小波网络适用于油膜参数辨识,本研究为发动机瞬态工况空燃比的精确控制奠定了基础。
A transient fuel film identification model for gasoline engines was built based on wavelet networks.The ant colony optimization(ACO)was used to initialize wavelet networks parameters as the initial values for the model so as to improve its convergence and precision.Transient air-fuel ratio was tested with this model,simulated with experimental data and compared with the actual air-fuel ratio.Results show that the model initialized with the ACO can effectively and precisely identify the transient fuel film with faster convergence and better generalization.Therefore,such model establishes a foundation for accurate control of transient air-fuel ratio of gasoline engines.
A transient fuel film identification model for gasoline engines was built based on wavelet networks.The ant colony optimization(ACO)was used to initialize wavelet networks parameters as the initial values for the model so as to improve its convergence and precision.Transient air-fuel ratio was tested with this model,simulated with experimental data and compared with the actual air-fuel ratio.Results show that the model initialized with the ACO can effectively and precisely identify the transient fuel film with faster convergence and better generalization.Therefore,such model establishes a foundation for accurate control of transient air-fuel ratio of gasoline engines.
引文
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[14]卢娜,肖志怀,符向前.基于蚁群初始化小波网络的水电机组振动故障诊断[J].水力发电学报,2014,32(2):251-258.LU N,XIAO Z H,FU X Q.Vibration fault diagnosis of hydroelectric generating unit based on ACO-initialized wavelet network[J].Journal of Hydroelectric Engineering,2014,32(2):251-258.
[2]李顶根,李小中.电喷汽油机油膜效应动态参数辨识精度的研究[J].内燃机学报,2010,28(5):453-458.LI D G,LI X Z.Study on accuracy identification of fuel film dynamic parameters in an electronically controlled gasoline engine[J].Transactions of CSICE,2010,28(5):453-458.
[3]李顶根,舒咏强.汽油机进气道油膜模型参数辨识算法的研究[J].内燃机学报,2009,27(4):363-369.LI D G,SHU Y Q.Research of parameter identification algorithm of intake port fuel film model for gasoline engine[J].Transactions of CSICE,2009,27(4):363-369.
[4]洪木南,李建秋,欧阳明高.汽油机油膜模型参数辨识[J].农业机械学报,2010,41(11):26-30.HONG M N,LI J Q,OUYANG M G.Parameters identification of the fuel film model for spark ignition engines[J].Transactions of the Chinese Society of Agricultural Machinery,2010,41(11):26-30.
[5]陈林林,魏民祥,杨海青.汽油发动机瞬态工况油膜参数辨识的研究[J].电子科技大学学报,2009,38(4):636-640.CHEN L L,WEI M X,YANG H Q.Parameter identification of fuel film model transient condition for gasoline engine[J].Journal of University of Electronic Science and Technology of China,2009,38(4):636-640.
[6]侯志祥,吴义虎,邓华,等.车用汽油机过渡工况空燃比的神经网络控制研究[J].内燃机工程,2006,27(5):33-36.HOU Z X,WU Y H,DENG H,et al.Air fuel ratio control of gasoline engine under transient condition based on neural networks[J].Chinese Internal Combustion Engine Engineering,2006,27(5):33-36.
[7]徐东辉,李岳林,解福泉,等.基于混沌RBF神经网络的汽油机瞬态工况油膜参数辨识研究[J].内燃机工程,2015,36(3):100-105.XU D H,LI Y L,XIE F Q,et al.Study of oil film parameter identification in gasoline engine transient conditions based on chaos-RBF neural network[J].Chinese Internal Combustion Engine Engineering,2015,36(3):100-105.
[8]CHEVALIER A,HENDRICK E.Predicting the port air mass flow of SI engine in air fuel ratio control application[C]//SAE Paper.Detroit,MI,USA,2000,2000-01-0260.
[9]ZHAN Y,SHEM T,JIAO X.Air-fuel ratio transient control design for gasoline engines based on individual cylinder air charge estimation[R].Bangkok,Thailand:Asia Pacific Automotive Engineering Conference,2013.
[10]ZHANG Q H,BENVENISTE A.A wavelet networks[J].IEEE Trans on Neural Networks,1992,3(6):889-898.
[11]王群仙,陈增强,袁著祉.基于小波网络的非线性系统建模与控制[J].控制与决策,1999,14(4):359-363.
[12]杨立才,贾磊,何立琴,等.基于混沌小波网络的交通流预测算法研究[J].山东大学学报(工学版),2005,35(2):46-50.YANG L C,JIA L,HE L Q,et al.Study of traffic flow forecast algorithms based on chaotic wavelet networks[J].Journal of Shandong University(Engineering Science),2005,35(2):46-50.
[13]谭云亮,肖亚勋,孙伟芳.煤与瓦斯突出自适应小波基神经网络辨识和预测模型[J].岩石力学与工程学报,2007,26(7):3373-3377.TAN Y L,XIAO Y X,SUN W F.Self-adaptive wavelet neural network for recognizing and predicating outburst of coal and gas[J].Chinese Journal of Rock Mechanics and Engineering,2007,26(7):3373-3377.
[14]卢娜,肖志怀,符向前.基于蚁群初始化小波网络的水电机组振动故障诊断[J].水力发电学报,2014,32(2):251-258.LU N,XIAO Z H,FU X Q.Vibration fault diagnosis of hydroelectric generating unit based on ACO-initialized wavelet network[J].Journal of Hydroelectric Engineering,2014,32(2):251-258.