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| An Online Decoupling Method for Cross-directional Basis Weight of Paper Machine Based on ELMAN Neural Network |
| Received:October 31, 2022 |
| DOI：10.11981/j.issn.1000-6842.2023.04.67 |
| Key Words:cross-directional basis weight of paper machine;non-square high-dimensional matrix;strong coupling;ELMAN neural network;online decoupling |
| Fund Project:陕西省技术创新引导专项（重点研发计划）“高速造纸机稀释水水力式流浆箱智能控制系统”（2023GXLH-071）。 |
| Author Name | Affiliation | Postcode | | TANG Wei | School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 | 710021 | | ZHANG Xu | School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 | 710021 | | SHEN Yunzhu | School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 | 710021 | | LIU Wenbo | School of Electrical and Control Engineering， Shaanxi University of Science & Technology， Xi’an， Shaanxi Province， 710021 | 710021 | | SHAN Wenjuan | Faculty of Science， Xi’an Aeronautical Institute， Xi’an， Shaanxi Province， 710077 | 710077 |
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| Abstract: |
| Aiming at the non-square and high-dimensional strong coupling characteristics of Cross-directional Basis Weight of Paper Machine， the paper proposes a feed-forward compensation online decoupling strategy based on ELMAN neural network. The idea is as follows： first， the system is reduced by introducing a matrix of prior transformation factors， and then the decoupling structure is designed by combining ELMAN neural network and feed-forward decoupling for the decoupled system. On the basis of that， a reference model is introduced and the network parameters are adjusted online according to the differences between the output of the reference model and the system， and the system coupling is compensated in real time to achieve online decoupling of the system. This transforms the non-square high-dimensional control problem into a single-loop group control problem， which greatly reduces the control difficulty of multi-input， multi-output， and high-dimensional systems. Simulation experiments and practical applications in the field have demonstrated the feasibility and effectiveness of the solution proposed in this paper， with a 42% improvement in quantitative control accuracy and a reduction in the error of Cross-directional Basis Weight of Paper Machine to 1.88%. |
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