Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

doi:10.1016/j.cjche.2014.05.002

›› 2014, Vol. 22 ›› Issue (7): 795-798.DOI: 10.1016/j.cjche.2014.05.002

• PROCESS MODEL • 上一篇下一篇

Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

Jie You, Qinmin Yang, Jiangang Lu, Youxian Sun

State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

收稿日期:2013-06-05 修回日期:2013-11-12 出版日期:2014-07-28 发布日期:2014-08-23
通讯作者: Jiangang Lu
基金资助:
Supported by the National Natural Science Foundation of China (21076179, 61104008), and National Basic Research Program of China (2012CB720500).

Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

Jie You, Qinmin Yang, Jiangang Lu, Youxian Sun

State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received:2013-06-05 Revised:2013-11-12 Online:2014-07-28 Published:2014-08-23
Supported by:
Supported by the National Natural Science Foundation of China (21076179, 61104008), and National Basic Research Program of China (2012CB720500).

摘要/Abstract

摘要： In this paper, asymmetric Gaussian weighting functions are introduced for the identification of linear parameter varying systems by utilizing an input-output multi-model structure. It is not required to select operating points with uniform spacing andmore flexibility is achieved. To verify the effectiveness of the proposed approach, severalweighting functions, including linear, Gaussian and asymmetric Gaussianweighting functions, are evaluated and compared. It is demonstrated through simulations with a continuous stirred tank reactor model that the proposed approach provides more satisfactory approximation.

关键词: Identification, Multi-model linear parameter varying system, Asymmetric Gaussian weight, Continuous stirred tank reactor

Abstract: In this paper, asymmetric Gaussian weighting functions are introduced for the identification of linear parameter varying systems by utilizing an input-output multi-model structure. It is not required to select operating points with uniform spacing andmore flexibility is achieved. To verify the effectiveness of the proposed approach, severalweighting functions, including linear, Gaussian and asymmetric Gaussianweighting functions, are evaluated and compared. It is demonstrated through simulations with a continuous stirred tank reactor model that the proposed approach provides more satisfactory approximation.

Key words: Identification, Multi-model linear parameter varying system, Asymmetric Gaussian weight, Continuous stirred tank reactor

Jie You, Qinmin Yang, Jiangang Lu, Youxian Sun. Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights[J]. , 2014, 22(7): 795-798.

参考文献

[1] Y. Zhao, B. Huang, H. Su, J. Chu, Prediction error method for identification of LPV models, J. Process Control 22 (1) (2012) 180-193.
[2] T. Proll, M.N. Karim, Model-predictive pH control using real time NARX approach, AIChE J. 2 (40) (1994) 269-282.
[3] S. Piche, B. Sayyar-Rodsari, D. Johnson, M. Gerules, Nonlinear model predictive control using neural networks, IEEE Control. Syst. Mag. 3 (20) (2000) 53-62.
[4] R.K. Person, M. Pottmann, Gray-box identification of block-oriented nonlinear models, J. Process Control 10 (4) (2000) 301-315.
[5] V. Laurain, M. Cilson, R. Toth, H. Garnier, Refined instrumental variable methods for identification for LPV Box-Jenkins models, Automatica 46 (2010) 959-967.
[6] J. Shamma, M. Athans, Guaranteed properties of gain scheduled control for linear parameter varying plants, Automatica 27 (1991) 559-564.
[7] V. Verdult,M. Verhaegen, Subspace identification ofmultivariable linear parametervarying systems, Automatica 38 (5) (2002) 805-814.
[8] B. Bamieh, L. Giarre, Identification of linear parameter varying models, Int. J. Robust Nonlinear Control 12 (2002) 841-853.
[9] J.Y. Shin, Worst-case analysis and linear parameter-varying gain-scheduled control of aerospace systems, (Ph.D. thesis) University of Minnesota, Minneapolis, USA, 2000.
[10] Y.C. Zhu, Z. Xu, A method of LPV model identification for control, Proceedings of the 17th IFAC World Congress, Seoul, Korea, 2008.
[11] X. Jin, B. Huang, D.S. Shook,Multiple model LPV approach to nonlinear process identification with EM algorithm, J. Process Control 21 (2011) 182-193.
[12] J.Y. Huang, G.L. Ji, Y.C. Zhu, P.V.D. Bosch, Identification of multi-model LPV models with two scheduling variables, J. Process Control 22 (7) (2012) 1198-1208.
[13] A. Boukhris, G. Mourot, J. Ragot, Non-linear dynamic system identification: a multimodel approach, Int. J. Control 72 (1999) 591-604.
[14] D.H. Hong, C. Hwang, C. Ahn, Ridge estimation for regression models with crisp inputs and Gaussian fuzzy output, Fuzzy Sets Syst. 142 (2004) 307-319.
[15] Y.C. Zhu, Multivariable System Identification for Process Control, Elsevier, London, UK, 2001.

Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

Identification of LPV Models with Non-uniformly Spaced Operating Points by Using Asymmetric Gaussian Weights

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Weipeng Lu, Xuefeng Yan. Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring[J]. 中国化学工程学报, 2021, 36(8): 128-137.
[2]	Xia Chen, Yan Wang, Lianying Wu, Weitao Zhang, Yangdong Hu. Testing and validation of a self-diffusion coefficient model based on molecular dynamics simulations[J]. 中国化学工程学报, 2021, 36(8): 138-145.
[3]	Denglong Ma, Jianmin Gao, Zhiyong Gao, Hongquan Jiang, Zaoxiao Zhang, Juntai Xie. Gas leakage recognition for CO₂ geological sequestration based on the time series neural network[J]. 中国化学工程学报, 2020, 28(9): 2343-2357.
[4]	Wende Tian, Zijian Liu, Lening Li, Shifa Zhang, Chuankun Li. Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning[J]. 中国化学工程学报, 2020, 28(7): 1875-1883.
[5]	Musango Lungu, Haotong Wang, Jingdai Wang, Ronald Ngulube, Yongrong Yang, Fengqiu Chen, John Siame. Assessment of the TFM in predicting the onset of turbulent fluidization[J]. 中国化学工程学报, 2019, 27(5): 979-992.
[6]	Yaoyao Bao, Yuanming Zhu, Weimin Zhong, Feng Qian. A novel chemical composition estimation model for cement raw material blending process[J]. 中国化学工程学报, 2019, 27(11): 2734-2741.
[7]	Denglong Ma, Wei Tan, Zaoxiao Zhang, Jun Hu. Gas emission source term estimation with 1-step nonlinear partial swarm optimization-Tikhonov regularization hybrid method[J]. Chinese Journal of Chemical Engineering, 2018, 26(2): 356-363.
[8]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics[J]. Chinese Journal of Chemical Engineering, 2018, 26(1): 137-143.
[9]	Guozhu Wang, Jianchang Liu, Yuan Li, Cheng Zhang. Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy[J]. , 2016, 24(7): 869-880.
[10]	Hongwei Li, Junpeng Liu, Yunlong Zhou, Bin Sun. Analysis of the nonlinear dynamic characteristics of two-phase flow based on an improved matrix pencil method[J]. Chinese Journal of Chemical Engineering, 2016, 24(6): 737-748.
[11]	Quanshan Li, Dazi Li, Liulin Cao. Closed-loop identification of systems using hybrid Box-Jenkins structure and its application to PID tuning[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 1997-2004.
[12]	Defeng He, Shiming Yu, Li Yu. Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes[J]. Chinese Journal of Chemical Engineering, 2015, 23(10): 1662-1669.
[13]	曹鹏飞, 罗雄麟. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. Chinese Journal of Chemical Engineering, 2014, 22(5): 538-548.
[14]	Lianfang Cai, Xuemin Tian, Ni Zhang. A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring[J]. , 2014, 22(11/12): 1243-1253.
[15]	Hongwei Wang, Tao Liu . Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition[J]. , 2014, 22(11/12): 1268-1273.