A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems

For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation...

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Veröffentlicht in:	International journal of control, automation, and systems Jg. 15; H. 3; S. 1189 - 1197
Hauptverfasser:	Pan, Jian, Jiang, Xiao, Wan, Xiangkui, Ding, Wenfang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.06.2017 Springer Nature B.V 제어·로봇·시스템학회
Schlagworte:	Algorithms Autoregressive moving average Computer simulation Control Control systems Engineering Filtering systems Filtration Innovations Mathematical models Mechatronics Multivariable control Parameter estimation Parameter identification Regular Papers Robotics Simulation Stochastic models Studies 제어계측공학 stochastic system parameter estimation filtering gradient search Estimation algorithm multi-innovation identification
ISSN:	1598-6446, 2005-4092
Online-Zugang:	Volltext
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Abstract	For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key is using the filtering technique and the multi-innovation identification theory. The proposed algorithms can identify the parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm can give more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithms work well.
AbstractList	For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key is using the filtering technique and the multi-innovation identification theory. The proposed algorithms can identify the parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm can give more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithms work well. For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressivemoving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm anda filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key isusing the filtering technique and the multi-innovation identification theory. The proposed algorithms can identifythe parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm cangive more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithmswork well. KCI Citation Count: 237
Author	Pan, Jian Jiang, Xiao Ding, Wenfang Wan, Xiangkui
Author_xml	– sequence: 1 givenname: Jian surname: Pan fullname: Pan, Jian email: jpan@163.com organization: School of Electrical and Electronic Engineering, Hubei University of Technology – sequence: 2 givenname: Xiao surname: Jiang fullname: Jiang, Xiao organization: School of Electrical and Electronic Engineering, Hubei University of Technology – sequence: 3 givenname: Xiangkui surname: Wan fullname: Wan, Xiangkui organization: School of Electrical and Electronic Engineering, Hubei University of Technology – sequence: 4 givenname: Wenfang surname: Ding fullname: Ding, Wenfang organization: School of Electrical and Electronic Engineering, Hubei University of Technology
BackLink	https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002225326$$DAccess content in National Research Foundation of Korea (NRF)
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Copyright	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017 International Journal of Control, Automation and Systems is a copyright of Springer, 2017.
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Snippet	For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering... For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressivemoving average system), this paper proposes a filtering...
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SubjectTerms	Algorithms Autoregressive moving average Computer simulation Control Control systems Engineering Filtering systems Filtration Innovations Mathematical models Mechatronics Multivariable control Parameter estimation Parameter identification Regular Papers Robotics Simulation Stochastic models Studies 제어계측공학
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