LAPSE:2021.0366
Published Article
LAPSE:2021.0366
A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System
Yuan Liu, Song Xu, Seiji Hashimoto, Takahiro Kawaguchi
May 17, 2021
Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.
Keywords
multi-input multi-output temperature system, neural network control, temperature uniformity, transient response
Suggested Citation
Liu Y, Xu S, Hashimoto S, Kawaguchi T. A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System. (2021). LAPSE:2021.0366
Author Affiliations
Liu Y: Division of Electronics and Informatics, Gunma University, 1-5-1 Tenjincho, Kiryu 376-8515, Japan [ORCID]
Xu S: Division of Electronics and Informatics, Gunma University, 1-5-1 Tenjincho, Kiryu 376-8515, Japan; Department of Electronics and Informatics, Jiangsu University of Science and Technology N.2 Mengxi Road, Zhenjiang 212000, China [ORCID]
Hashimoto S: Division of Electronics and Informatics, Gunma University, 1-5-1 Tenjincho, Kiryu 376-8515, Japan
Kawaguchi T: Division of Electronics and Informatics, Gunma University, 1-5-1 Tenjincho, Kiryu 376-8515, Japan
Journal Name
Processes
Volume
8
Issue
11
Article Number
E1365
Year
2020
Publication Date
2020-10-28
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8111365, Publication Type: Journal Article
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LAPSE:2021.0366
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doi:10.3390/pr8111365
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May 17, 2021
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May 17, 2021
 
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May 17, 2021
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Calvin Tsay
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