Simultaneous Identification of Parameter and Time-delay Based on Subspace Method and Cross-correlation Function
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- 英文论文题名：Simultaneous Identification of Parameter and Time-delay Based on Subspace Method and Cross-correlation Function
- 论文作者：Jian-Guo Wang ; Zhi-Fu Deng ; Bang-Hua Yang ; Shi-Wei Ma ; Min-Rui Fei ; Li-Lan Liu ; Yuan Yao ; Tao Chen ; Yi-Ping Wu
- 英文论文作者：Jian-Guo Wang ; Zhi-Fu Deng ; Bang-Hua Yang ; Shi-Wei Ma ; Min-Rui Fei ; Li-Lan Liu ; Yuan Yao ; Tao Chen ; Yi-Ping Wu ; School of Mechatronical Engineering and Automation ; Shanghai University ; Shanghai Key Lab of Power Station Automation Technology ; Department of Chemical Engineering ; "Tsing-Hua University" ; Department of Chemical and Process Engineering ; University of Surrey ; Shanghai Baosight Software Co Ltd
- 年：2017
- 作者机构：School of Mechatronical Engineering and Automation,Shanghai University,Shanghai Key Lab of Power Station Automation Technology;Department of Chemical Engineering,"Tsing-Hua University";Department of Chemical and Process Engineering,University of Surrey;Shanghai Baosight Software Co Ltd;
- 英文论文关键词：Subspace Method ; Cross-Correlation ; Time-Varying ; 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：TP13
- 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：6
- 文件大小：640k
- 原文格式：D
- 会议级别：全国
摘要
Both the plant parameter4 and delay time of the system may vary in the time-varying system, which makes system identification relatively complex. In this paper, a new system identification algorithm based on subspace method and cross-correlation function is proposed to identify system parameter and time-delay simultaneously. The time delay is identified with the calculation of the cross-correlation function between pre-processed input data that processed through initial plant parameters and original output data. Then, the time delay in the output data can be deleted with the time delay identified before and the parameters can be achieved by subspace identification algorithm. Therefore, the process parameters and time delay identification can be performed online by through above two alternative operations. The validity of the proposed identification algorithm is verified by the simulation study.
Both the plant parameter4 and delay time of the system may vary in the time-varying system, which makes system identification relatively complex. In this paper, a new system identification algorithm based on subspace method and cross-correlation function is proposed to identify system parameter and time-delay simultaneously. The time delay is identified with the calculation of the cross-correlation function between pre-processed input data that processed through initial plant parameters and original output data. Then, the time delay in the output data can be deleted with the time delay identified before and the parameters can be achieved by subspace identification algorithm. Therefore, the process parameters and time delay identification can be performed online by through above two alternative operations. The validity of the proposed identification algorithm is verified by the simulation study.
Both the plant parameter4 and delay time of the system may vary in the time-varying system, which makes system identification relatively complex. In this paper, a new system identification algorithm based on subspace method and cross-correlation function is proposed to identify system parameter and time-delay simultaneously. The time delay is identified with the calculation of the cross-correlation function between pre-processed input data that processed through initial plant parameters and original output data. Then, the time delay in the output data can be deleted with the time delay identified before and the parameters can be achieved by subspace identification algorithm. Therefore, the process parameters and time delay identification can be performed online by through above two alternative operations. The validity of the proposed identification algorithm is verified by the simulation study.
引文
[1]S.Bedoui,M.Ltaief,K.Abderrahim,R.Ben Abdennour,Representation and control of time delay system:Multi-model approach,in Proceedings of the 8th International Multi-conference on Systems,Signals and Devices,2011:1-6.
[2]H.Kurz,W.Goedecke,Digital parameter-adaptive control of process with unknown dead time,Automatica,17(1):245-252,1981.
[3]S.W.Sung,I.B.Lee,Prediction error identification method for continuous-time processes with time delay,Industrial and Engineering Chemistry Research,40(24):5743-5751,2001.
[4]O.Gomez,Y.Orlov,I.V.Kolmanovsky,On-line identification of SISO linear time-invariant delay systems from output measurements.Automatica,43(12):2060-2069,2007.
[5]S.Ahmed,B.Huang,S.L.Shah,Parameter and delay estimation of continuous-time models using a linear filter,Journal of Process Control,16(4):323-331,2006
[6]A.B.Rad,W.L.Lo,K.M.Tsang,Simultaneous online identification of rational dynamics and time delay:A correlation-based approach,IEEE Trans.on Control Systems Technology,11(6):957-959,2003
[7]T.Zhang,Y.C.Li,A fuzzy smith control of time-varying delay systems based on time delay identification.In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics,2003:614-619.
[8]W.X.Zheng,C.B.Feng,Identification of stochastic time lag systems in the presence of colored noise.Automatica,26(4):769-779,1990.
[9]W.Gao,M.L.Zhou,Y.C.Li,T.Zhang,An adaptive generalized predictive control of time-varying delay system.In Proceedings of the 2nd International Conference on Machine Learning and Cybernetics,2004:878-881.
[10]Gu Wei-Dong,Sun Zhi-Yong,Wu Xiao-Ming,Yu Chang-Bin,Simultaneous identification of unknown time delays and model parameters in uncertain dynamical systems with linear or nonlinear parameterization by auto-synchronization[J].Chinese Physics B,22(9),2013.
[2]H.Kurz,W.Goedecke,Digital parameter-adaptive control of process with unknown dead time,Automatica,17(1):245-252,1981.
[3]S.W.Sung,I.B.Lee,Prediction error identification method for continuous-time processes with time delay,Industrial and Engineering Chemistry Research,40(24):5743-5751,2001.
[4]O.Gomez,Y.Orlov,I.V.Kolmanovsky,On-line identification of SISO linear time-invariant delay systems from output measurements.Automatica,43(12):2060-2069,2007.
[5]S.Ahmed,B.Huang,S.L.Shah,Parameter and delay estimation of continuous-time models using a linear filter,Journal of Process Control,16(4):323-331,2006
[6]A.B.Rad,W.L.Lo,K.M.Tsang,Simultaneous online identification of rational dynamics and time delay:A correlation-based approach,IEEE Trans.on Control Systems Technology,11(6):957-959,2003
[7]T.Zhang,Y.C.Li,A fuzzy smith control of time-varying delay systems based on time delay identification.In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics,2003:614-619.
[8]W.X.Zheng,C.B.Feng,Identification of stochastic time lag systems in the presence of colored noise.Automatica,26(4):769-779,1990.
[9]W.Gao,M.L.Zhou,Y.C.Li,T.Zhang,An adaptive generalized predictive control of time-varying delay system.In Proceedings of the 2nd International Conference on Machine Learning and Cybernetics,2004:878-881.
[10]Gu Wei-Dong,Sun Zhi-Yong,Wu Xiao-Ming,Yu Chang-Bin,Simultaneous identification of unknown time delays and model parameters in uncertain dynamical systems with linear or nonlinear parameterization by auto-synchronization[J].Chinese Physics B,22(9),2013.