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Maximum Likelihood-based Multi-innovation Stochastic Gradient Method for Multivariable Systems

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Abstract

This paper considers the parameter estimation problems for multivariable controlled autoregressive moving average systems. By means of the decomposition technique, a multivariable system is transformed into several identification submodels according to the number of outputs. A maximum likelihood extended stochastic gradient identification algorithm is derived for identifying each subsystem by using the maximum likelihood principle. In order to improve the convergence rate, a multivariable maximum likelihood-based muti-innovation stochastic gradient algorithm is proposed. The proposed algorithms can generate more accurate parameter estimates compared with the multivariable extended stochastic gradient algorithm. The illustrative simulation results show that the proposed methods work well.

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Author information

Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P. R. China

    Huafeng Xia, Yanjun Liu & Ling Xu

  2. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266061, P. R. China

    Huafeng Xia & Yan Ji

  3. Taizhou Electric Power Conversion and Control Engineering Technology Research Center, Taizhou University, Taizhou, 225300, P. R. China

    Huafeng Xia & Yan Ji

  4. Department of Mathematics, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Ling Xu

Authors
  1. Huafeng Xia
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  2. Yan Ji
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  3. Yanjun Liu
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Corresponding author

Correspondence to Huafeng Xia.

Additional information

Recommended by Associate Editor Huaping Liu under the direction of Editor Young IL Lee. This work was supported by the National Natural Science Foundation of China (No. 61873111), 135 Engineering of Taizhou Education Bureau (No. 2018TZCJ001), the Qing Lan Project and the Postdoctoral Science Foundation of Jiangsu Province (No. 1701020A). The authors are grateful to Professor Feng Ding at the Jiangnan University (Wuxi, China) for his helpful suggestions.

Huafeng Xia received her B.Sc. degree from the Jiangsu University of Technology (Changzhou, China) in 2003, the M.Sc. degree from Wuhan University of Science and Technology (Wuhan, China) in 2008. From 2003 to 2005, she was a teacher in Jiangyin Huazi Vocatinal School, Jiangyin, Jiangsu Province. From 2008 to now, she is a teacher in Taizhou University, Taizhou, Jiangsu Province. She is currently a Ph.D. student in the School of Internet of Things Engineering, Jiangnan University, (Wuxi, China). Her interests include system modeling, system identification and process control.

Yan Ji received her Ph.D. degree from the School of Communication and Control Engineering, Jiangnan University (Wuxi, China) in 2010. She has been an Associate Professor in the College of Automation and Electronic Engineering, Qingdao University of Science and Technology. Her research interests include control of complex networks, system identification and process control.

Yanjun Liu received the B.Sc. degree from the Jiangsu University of Technology (Changzhou, China) in 2003, the M.Sc. and Ph.D. degrees from Jiangnan University (Wuxi, China), in 2009 and 2012, respectively. From 2003 to 2007, she was a teacher in the Jiangyin Huazi Vocational School, Jiangyin, Jiangsu Province. From 2010 to 2011, she was a visiting Ph.D. student in the Department of Mechanical Engineering at the University of Victoria, Canada. She is currently an Associate Professor in the School of Internet of Things Engineering, Jiangnan University, Wuxi, China. Her research interests include system identification and parameter estimation.

Ling Xu was born in Tianjin, China. She received the Master and Ph.D. degrees from the Jiangnan University (Wuxi, China), in 2005 and 2015, respectively. She has been an Associate Professor since 2015. She is a Colleges and Universities “Blue Project” Young Teacher (Jiangsu, China). Her research interests include process control, parameter estimation and signal modeling.

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Xia, H., Ji, Y., Liu, Y. et al. Maximum Likelihood-based Multi-innovation Stochastic Gradient Method for Multivariable Systems. Int. J. Control Autom. Syst. 17, 565–574 (2019). https://doi.org/10.1007/s12555-018-0135-5

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  • DOI: https://doi.org/10.1007/s12555-018-0135-5

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