Two-dimensional identification of time-varying batch nonlinear processes with input dead-zone nonlinearity

ABSTRACT

In the time dimension, the batch process with input dead-zone nonlinearity often has the characteristics of short operation time and time-varying process parameters. A two-dimensional recursive least squares (2D-RLS) identification method for time-varying nonlinear systems with input dead zone is proposed. By mining system dynamic information from the time direction of the same batch and different batch directions, the identification parameters are updated. The proposed two-dimensional identification strategy converts the time-varying parameter estimation problem of time dimension into the equivalent time-invariant parameter estimation problem of batch dimension. Sufficient production data in batch dimension ensure that the proposed identification algorithm can eliminate the influence of random measurement noise. The identification strategy with unequal length of batch data is also given. The convergence of the proposed algorithm is analysed. Finally, numerical simulation and experimental tests are used to verify the effectiveness and superiority of the proposed algorithm.

KEYWORDS:

• Batch process
• two-dimensional identification
• parameter estimation
• dead-zone nonlinearity
• convergence

Disclosure statement

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

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 52072343]; the Jiangsu Provincial Natural Science Foundation of China [grant number BK20210493]; the Fundamental Research Funds for the Central Universities [grant number 2022QN1048]; and the Joint Program of Science and Technology Plans in Liaoning Province [grant number 2023JH2/101700302].

Notes on contributors

Shijian Dong

Shijian Dong (Member, IEEE) received his B.Sc. and M.Sc. degrees from the Northeastern University, Shenyang, China, in 2011 and 2013, respectively. He received his Ph.D. degree in control theory and control engineering from the Dalian University of Technology, Dalian, China, in 2018. He is currently a lecturer at the School of Metallurgy, Northeastern University, Shenyang, China. His main research interests include system modelling, optimisation and predictive control. His email address is [email protected].

Yuzhu Zhang

Yuzhu Zhang was born in JinNing, China. He is currently a student at the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. His research interests are intelligent modelling and intelligent control of industrial processes. His email address is [email protected].

Xingxing Zhou

Xingxing Zhou was born in Yichun, China. He is currently a student at the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. His research interests include artificial intelligence and industrial control. His email address is [email protected].

Leilei Hao

Leilei Hao was born in Linquan, China. He is currently a student at the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. His research interests include artificial intelligence and industrial process controller design. His email address is [email protected].