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Maximum likelihood estimation method for dual-rate Hammerstein systems

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  • Control Theory and Applications
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Abstract

For a dual-rate sampled Hammerstein controlled autoregressive moving average (CARMA) system, this paper uses the polynomial transformation technology to obtain its dual-rate bilinear identification model which is suitable for the available dual-rate sampled-data, uses the maximum likelihood principle to construct a unified parameter vector of all parameters and an information vector formed by the derivative of the noise variable to the unified parameter vector, and directly identifies the parameters of the linear block and the nonlinear block for the dual-rate Hammerstein CARMA system. The unified parameter vector contains the minimum number of the unknown parameters, and the proposed maximum likelihood estimation algorithm has higher computational efficiency than the over-parameterization model based least squares algorithm.

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Authors and Affiliations

  1. College of Automation and Electrical Engineering, Qingdao University, Qingdao, 266071, P. R. China

    Dong-Qing Wang, Zhen Zhang & Jin-Yun Yuan

Authors
  1. Dong-Qing Wang
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  2. Zhen Zhang
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  3. Jin-Yun Yuan
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Corresponding author

Correspondence to Dong-Qing Wang.

Additional information

Recommended by Associate Editor Choon Ki Ahn under the direction of Editor Yoshito Ohta. This work was supported by the National Natural Science Foundation of China under grant 61573205, 61403217, and the Shandong Provincial Natural Science Foundation of China under grant ZR2015FM017.

Dong-Qing Wang received the Ph.D. degree from the School of Electrical Engineering and Automation, Tianjin University, China in 2006. She was a Visiting Scholar in the Department of Electrical and Computer Engineering at the University of Tennessee, Knoxville, USA during 2004. Since December 2010, she has been a Full Professor in the College of Automation and Electrical Engineering at Qingdao University, China. Her research interests include process modeling, system identification, and adaptive control.

Zhen Zhang received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, China in 2013. He is currently a Lecturer in the College of Automation and Electrical Engineering at Qingdao University, China. His research interests cover reinforcement learning, multiagent reinforcement learning, and intelligent urban traffic signal control.

Jin-Yun Yuan received the M.S. degree from the College of Automation and Electrical Engineering at Qingdao University, China in 2016. Her research interests include nonlinear system identification and adaptive control.

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Wang, DQ., Zhang, Z. & Yuan, JY. Maximum likelihood estimation method for dual-rate Hammerstein systems. Int. J. Control Autom. Syst. 15, 698–705 (2017). https://doi.org/10.1007/s12555-015-0371-x

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  • DOI: https://doi.org/10.1007/s12555-015-0371-x

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