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Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems

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  • Control Theory and Applications
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Abstract

With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, P. R. China

    Bingbing Shen, Feng Ding & Ling Xu

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

    Feng Ding

  3. NAAM Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

    Feng Ding & Tasawar Hayat

Authors
  1. Bingbing Shen
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  2. Feng Ding
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  3. Ling Xu
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  4. Tasawar Hayat
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Corresponding author

Correspondence to Feng Ding.

Additional information

Recommended by Associate Editor Xiaojie Su under the direction of Editor Hamid Reza Karimi. This work was supported by the 111 Project (No. B12018), the Jiangsu Province Industry University Prospective Joint Research Project (BY2015019-29), the Fundamental Research Funds for the Central Universities (JUSRP51733B) and the National Natural Science Foundation of China (No. 61472195).

Bingbing Shen was born in Yancheng (Jiangsu Province, China) in 1992. She received her B.Sc degree in the Jiangnan University (Wuxi, China) in 2015. She is currently a master student in the School of Internet of Things Engineering at the Jiangnan University (Wuxi, China). Her research interests include system identification and process control.

Feng Ding received his B.Sc. degree from the Hubei University of Technology (Wuhan, China) in 1984, and his M.Sc. and Ph.D. degrees both from the Tsinghua University, in 1991 and 1994, respectively. He has been a professor in the School of Internet of Things Engineering at the Jiangnan University (Wuxi, China) since 2004. His current research interests include model identification and adaptive control. He authored four books on System Identification.

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.

Tasawar Hayat was born in Khanewal, Punjab, Distinguished National Professor and Chairperson of Mathematics Department at Quaid-I-Azam University is renowned worldwide for his seminal, diversified and fundamental contributions in models relevant to physiological systems, control engineering. He has a honor of being fellow of Pakistan Academy of Sciences, Third World Academy of Sciences (TWAS) and Islamic World Academy of Sciences in the mathematical Sciences.

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Shen, B., Ding, F., Xu, L. et al. Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems. Int. J. Control Autom. Syst. 16, 2225–2234 (2018). https://doi.org/10.1007/s12555-017-0596-y

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  • DOI: https://doi.org/10.1007/s12555-017-0596-y

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