Coupled Identification Based Approximate Least Absolute Deviation for Multivariable System
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- 英文论文题名：Coupled Identification Based Approximate Least Absolute Deviation for Multivariable System
- 论文作者：Bao-chang Xu ; Li-kun Yuan ; Hua Zhang ; Wei Liu
- 英文论文作者：Bao-chang Xu ; Li-kun Yuan ; Hua Zhang ; Wei Liu ; Department of Automation ; China University of Petroleum(Beijing) ; CNPC Drilling Engineering Research Institute
- 年：2017
- 作者机构：Department of Automation,China University of Petroleum(Beijing);CNPC Drilling Engineering Research Institute;
- 英文论文关键词：approximate least absolute deviation ; multivariable system ; impulse noise ; coupled identification
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：N945.14
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：7
- 文件大小：234k
- 原文格式：D
- 会议级别：全国
摘要
Based on approximate least absolute deviation criterion and Newton's search principle, a full coupled identification algorithm is proposed for multivariable system whose model parameters are fully coupled. To solve the non-differentiable problem of the least absolute deviation, an approximate least absolute deviation objective function is established by introducing a deterministic differentiable function to replace the absolute residual. The proposed method can overcome the disadvantage of large square residual of least square criterion when the identification data is disturbed by the impulse noise which obeys symmetrical alpha stable(SaS) distribution. Moreover, a differentiable objective function avoids the operation of solving matrix inversion, which makes it easier to calculate. Simulation experiments show that the proposed full coupled identification algorithm can effectively identify the model parameters of multivariable system and restrain the impact of impulse noise.Satisfactory performance and strong robustness indicate the proposed multivariable identification algorithm could be well applied to practical industrial systems.
Based on approximate least absolute deviation criterion and Newton's search principle, a full coupled identification algorithm is proposed for multivariable system whose model parameters are fully coupled. To solve the non-differentiable problem of the least absolute deviation, an approximate least absolute deviation objective function is established by introducing a deterministic differentiable function to replace the absolute residual. The proposed method can overcome the disadvantage of large square residual of least square criterion when the identification data is disturbed by the impulse noise which obeys symmetrical alpha stable(SaS) distribution. Moreover, a differentiable objective function avoids the operation of solving matrix inversion, which makes it easier to calculate. Simulation experiments show that the proposed full coupled identification algorithm can effectively identify the model parameters of multivariable system and restrain the impact of impulse noise.Satisfactory performance and strong robustness indicate the proposed multivariable identification algorithm could be well applied to practical industrial systems.
Based on approximate least absolute deviation criterion and Newton's search principle, a full coupled identification algorithm is proposed for multivariable system whose model parameters are fully coupled. To solve the non-differentiable problem of the least absolute deviation, an approximate least absolute deviation objective function is established by introducing a deterministic differentiable function to replace the absolute residual. The proposed method can overcome the disadvantage of large square residual of least square criterion when the identification data is disturbed by the impulse noise which obeys symmetrical alpha stable(SaS) distribution. Moreover, a differentiable objective function avoids the operation of solving matrix inversion, which makes it easier to calculate. Simulation experiments show that the proposed full coupled identification algorithm can effectively identify the model parameters of multivariable system and restrain the impact of impulse noise.Satisfactory performance and strong robustness indicate the proposed multivariable identification algorithm could be well applied to practical industrial systems.
引文
[1]B.C.Xu,X.L.Liu.Research on identification algorithm based on the approximate least absolute deviation criteria.International Journal of Automation and Computing,9(5):501-505,2012.
[2]B.C.Xu,Y.D.Zhang.Multi-innovation identification algorithm based on approximate least absolute deviation.Control Engineering of China,22(1):60-65,2015.
[3]C.Z.Fang,D.Y.Xiao.Process Identification.Eds.Beijing:Tsinghua-University Press,1988:361-396.
[4]D.W.Gao,Y.Z.Peng,S.Y.Wang,et al.The research and application and development of artificial control in sewage treatment.Chins Water&Waste Water,18(6):35-3,2002.
[5]F.Ding,G Liu,X.P.Liu.Partially coupled stochastic gradient identification methods for non-uniformly sampled systems.IEEE Transactions on Automatic Control,55(8):1976-1981,2010.
[6]F.Ding.Coupled-least-squares identification for multivariable systems.IET Control Theory Appl.,7(1):68–79,2013.
[7]F.Ding,T.Chen.Hierarchical gradient-based identification of multivariable discrete-time systems.Automatica,41(2):315-325,2005.
[8]F.Ding,T.Chen.Identification of Hammerstein nonlinear ARMAX systems.Automatica,41(9):1479-1489,2005.
[9]F.Ding.System identification,Part H:Coupled identification concept and methods.Journal of Nanjing University of Information Science and Technology:Natural Science Edition,4(3):193-212,2012.
[10]Gopinath.On the identification of linear time-invariant systems from input-output data.The Bell System Technical Journal,48(5):1101-1113,1969.
[11]G.Y.Li,N.N.Gao,W.H.Xia.Application of recursive least square method in identification of dehydrogenation reactor.Journal of System Simulation,28(1):249-254,2016.
[12]H.Xiang,Z.H.Chen.New identification method of nonlinear systems based on Hammerstein models.Control Theory&Applications,24(1):143-147,2007.
[13]J.Dong,K.G.Xie.Research of the non-linear regress models based on the least absolute criteria.Journal of Chongqing Normal University(Natural Science Edition),18(4):71-74,2001.
[14]L.Deroye.Non-uniform random variant generation.Springer-Verlag,1986.
[15]M.Budin.Minimal realization of discrete system from input-output observation.IEEE Transactions on Automatic Control,16(5):395-401,1971.
[16]M.A.Dhaifallah,K.S.Nisar.Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine.Industrial Technology(ICIT),2015 IEEE International Conference on,Seville,2015:559-563.
[17]M.J.Coates,E.E.Kuruoglu.Time-frequency based detection in impulsive noise environments using alpha-stable noise model.Digital Signal Processing.82(3):1917-19252002.
[18]Q.Dong.Relaxation Algorithm based on“least absolute deviation”criterion.Predictive Method Research,9(2):16-181990.
[19]R.Gupta,F.Fairman.Parameter estimation for multivariable systems.IEEE Transactions on Automatic Control,19(5):546-549,1974.
[20]R.Guidorzi.Canonical structures in the identification of multivariable systems.Automatica,11(4):361-374,1975.
[21]X.L.Luo,X.Y.Zhao,B.Wu,et al.Online detection and control of ethylene column abnormal condition.CIESC Journal,65(11):4517-4523,2014.
[22]X.L.Luo,X.Zuo,C.H.Chen,et al.Process observer and advanced process control of ethylene rectifier.Control and Instruments in Chemical Industry,29(1):13-19,2002.
[23]Y.Zhang,H.Z.Yang.Bias compensation recursive least squares identification for output error systems with colored noise.ACTA Automatica Sinica,33(10):1053-1060,2007.
[2]B.C.Xu,Y.D.Zhang.Multi-innovation identification algorithm based on approximate least absolute deviation.Control Engineering of China,22(1):60-65,2015.
[3]C.Z.Fang,D.Y.Xiao.Process Identification.Eds.Beijing:Tsinghua-University Press,1988:361-396.
[4]D.W.Gao,Y.Z.Peng,S.Y.Wang,et al.The research and application and development of artificial control in sewage treatment.Chins Water&Waste Water,18(6):35-3,2002.
[5]F.Ding,G Liu,X.P.Liu.Partially coupled stochastic gradient identification methods for non-uniformly sampled systems.IEEE Transactions on Automatic Control,55(8):1976-1981,2010.
[6]F.Ding.Coupled-least-squares identification for multivariable systems.IET Control Theory Appl.,7(1):68–79,2013.
[7]F.Ding,T.Chen.Hierarchical gradient-based identification of multivariable discrete-time systems.Automatica,41(2):315-325,2005.
[8]F.Ding,T.Chen.Identification of Hammerstein nonlinear ARMAX systems.Automatica,41(9):1479-1489,2005.
[9]F.Ding.System identification,Part H:Coupled identification concept and methods.Journal of Nanjing University of Information Science and Technology:Natural Science Edition,4(3):193-212,2012.
[10]Gopinath.On the identification of linear time-invariant systems from input-output data.The Bell System Technical Journal,48(5):1101-1113,1969.
[11]G.Y.Li,N.N.Gao,W.H.Xia.Application of recursive least square method in identification of dehydrogenation reactor.Journal of System Simulation,28(1):249-254,2016.
[12]H.Xiang,Z.H.Chen.New identification method of nonlinear systems based on Hammerstein models.Control Theory&Applications,24(1):143-147,2007.
[13]J.Dong,K.G.Xie.Research of the non-linear regress models based on the least absolute criteria.Journal of Chongqing Normal University(Natural Science Edition),18(4):71-74,2001.
[14]L.Deroye.Non-uniform random variant generation.Springer-Verlag,1986.
[15]M.Budin.Minimal realization of discrete system from input-output observation.IEEE Transactions on Automatic Control,16(5):395-401,1971.
[16]M.A.Dhaifallah,K.S.Nisar.Wiener modeling and identification of a reverse osmosis desalination process using least square support vector machine.Industrial Technology(ICIT),2015 IEEE International Conference on,Seville,2015:559-563.
[17]M.J.Coates,E.E.Kuruoglu.Time-frequency based detection in impulsive noise environments using alpha-stable noise model.Digital Signal Processing.82(3):1917-19252002.
[18]Q.Dong.Relaxation Algorithm based on“least absolute deviation”criterion.Predictive Method Research,9(2):16-181990.
[19]R.Gupta,F.Fairman.Parameter estimation for multivariable systems.IEEE Transactions on Automatic Control,19(5):546-549,1974.
[20]R.Guidorzi.Canonical structures in the identification of multivariable systems.Automatica,11(4):361-374,1975.
[21]X.L.Luo,X.Y.Zhao,B.Wu,et al.Online detection and control of ethylene column abnormal condition.CIESC Journal,65(11):4517-4523,2014.
[22]X.L.Luo,X.Zuo,C.H.Chen,et al.Process observer and advanced process control of ethylene rectifier.Control and Instruments in Chemical Industry,29(1):13-19,2002.
[23]Y.Zhang,H.Z.Yang.Bias compensation recursive least squares identification for output error systems with colored noise.ACTA Automatica Sinica,33(10):1053-1060,2007.