Parameter tracking of time-varying Hammerstein-Wiener systems

Abstract

A two-stage identification algorithm is introduced for tracking the parameters in time-varying Hammerstein-Wiener systems. The Kalman filtering algorithm and parameter separation technique are employed in the proposed algorithm. The convergence analysis of this two-stage algorithm is provided. It is shown that the proposed algorithm can guarantee the boundedness of the parameter estimation error. Four simulation examples, including a practical system application of electric arc furnace, have been employed to validate the effectiveness of the proposed approaches, for a range of simulated time-varying characteristics.

KEYWORDS:

• Hammerstein-Wiener systems
• time-varying
• two-stage identification
• tracking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by China Scholarship Council [grant number 201906085064]; National Natural Science Foundation of China [grant numbers 61703085, 61773105]; Fundamental Research Funds for the Central Universities in China [grant number 170404021].

Notes on contributors

Feng Yu

Feng Yu received his university education at Northeastern University, People’s Republic of China (BSc, 2007, MSc, 2009, PhD, 2015), all in control theory and control engineering. He is currently a lecture at College of Information Science and Engineering, Northeastern University. His research interests include nonlinear systems identification, control and their applications.

Xia Hong

Xia Hong received her university education at National University of Defense Technology, People’s Republic of China (BSc, 1984, MSc, 1987), and University of Sheffield, UK (PhD, 1998), all in automatic control. She worked as a research assistant in Beijing Institute of Systems Engineering, Beijing, China from 1987–1993. She worked as a research fellow in the Department of Electronics and Computer Science at University of Southampton from 1997–2001. She is currently a Professor at Department of Computer Science, School of Mathematical and Physical Sciences, University of Reading. She is actively engaged in research into nonlinear systems identification, data modelling, estimation and intelligent control, neural networks, pattern recognition, learning theory and their applications. She has published over 200 research papers, and co-authored a research book. Professor Hong was awarded a Donald Julius Groen Prize by IMechE in 1999.