Model identification for closed-loop multivariable processes based on min-max critical frequency search
This paper presents an improved method of model parameters identification in frequency-domain for closed-loop multivariable processes. Based on reference input and process output data during the closed-loop sequence step tests, the process frequency-responses are estimated with signal frequency anal...
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sg-ntu-dr.10356-979202020-03-07T13:24:48Z Model identification for closed-loop multivariable processes based on min-max critical frequency search Luo, Yunhui Liu, Hongbo Cai, Wenjian Jia, Lei Jia, Zhiping Song, Ruifu School of Electrical and Electronic Engineering World Congress on Intelligent Control and Automation (10th : 2012 : Beijing, China) DRNTU::Engineering::Electrical and electronic engineering This paper presents an improved method of model parameters identification in frequency-domain for closed-loop multivariable processes. Based on reference input and process output data during the closed-loop sequence step tests, the process frequency-responses are estimated with signal frequency analysis. Using a min-max critical frequency search algorithm, only a least possible number of frequency points are obtained for model fitting. Then the first order plus delay time transfer functions are determined by implementing the linear least-square method. Compared with existing methods, the proposed identification technique has the advantage of less computation burden and is easy for industrial applications. Simulation results show the simplicity and effectiveness of the proposed method. 2013-07-25T07:51:14Z 2019-12-06T19:48:22Z 2013-07-25T07:51:14Z 2019-12-06T19:48:22Z 2012 2012 Conference Paper Luo, Y., Liu, H., Cai, W., Jia, L., Jia, Z., & Song, R. (2012). Model identification for closed-loop multivariable processes based on min-max critical frequency search. 2012 10th World Congress on Intelligent Control and Automation (WCICA). https://hdl.handle.net/10356/97920 http://hdl.handle.net/10220/12290 10.1109/WCICA.2012.6358435 en © 2012 IEEE. |
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DRNTU::Engineering::Electrical and electronic engineering Luo, Yunhui Liu, Hongbo Cai, Wenjian Jia, Lei Jia, Zhiping Song, Ruifu Model identification for closed-loop multivariable processes based on min-max critical frequency search |
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This paper presents an improved method of model parameters identification in frequency-domain for closed-loop multivariable processes. Based on reference input and process output data during the closed-loop sequence step tests, the process frequency-responses are estimated with signal frequency analysis. Using a min-max critical frequency search algorithm, only a least possible number of frequency points are obtained for model fitting. Then the first order plus delay time transfer functions are determined by implementing the linear least-square method. Compared with existing methods, the proposed identification technique has the advantage of less computation burden and is easy for industrial applications. Simulation results show the simplicity and effectiveness of the proposed method. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Luo, Yunhui Liu, Hongbo Cai, Wenjian Jia, Lei Jia, Zhiping Song, Ruifu |
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Conference or Workshop Item |
author |
Luo, Yunhui Liu, Hongbo Cai, Wenjian Jia, Lei Jia, Zhiping Song, Ruifu |
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Luo, Yunhui |
title |
Model identification for closed-loop multivariable processes based on min-max critical frequency search |
title_short |
Model identification for closed-loop multivariable processes based on min-max critical frequency search |
title_full |
Model identification for closed-loop multivariable processes based on min-max critical frequency search |
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Model identification for closed-loop multivariable processes based on min-max critical frequency search |
title_full_unstemmed |
Model identification for closed-loop multivariable processes based on min-max critical frequency search |
title_sort |
model identification for closed-loop multivariable processes based on min-max critical frequency search |
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2013 |
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https://hdl.handle.net/10356/97920 http://hdl.handle.net/10220/12290 |
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1681044207499739136 |