Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

doi:10.1016/j.cjche.2015.10.009

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (12): 2048-2052.DOI: 10.1016/j.cjche.2015.10.009

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Kai Feng, Jiangang Lu, Jinshui Chen

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

收稿日期:2015-06-01 修回日期:2015-09-07 出版日期:2015-12-28 发布日期:2016-01-19
通讯作者: Jiangang Lu
基金资助:
Supported by the National Natural Science Foundation of China (21076179) and the National Basic Research Program of China (2012CB720500).

Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Kai Feng, Jiangang Lu, Jinshui Chen

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

Received:2015-06-01 Revised:2015-09-07 Online:2015-12-28 Published:2016-01-19
Contact: Jiangang Lu
Supported by:
Supported by the National Natural Science Foundation of China (21076179) and the National Basic Research Program of China (2012CB720500).

摘要/Abstract

摘要： This paper presents a nonlinear model predictive control (NMPC) approach based on support vector machine (SVM) and genetic algorithm (GA) for multiple-input multiple-output (MIMO) nonlinear systems. Individual SVM is used to approximate each output of the controlled plant. Then the model is used in MPC control scheme to predict the outputs of the controlled plant. The optimal control sequence is calculated using GA with elite preserve strategy. Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

关键词: Support vector machine, Genetic algorithm, Nonlinear model predictive control, Neural network, Modeling

Abstract: This paper presents a nonlinear model predictive control (NMPC) approach based on support vector machine (SVM) and genetic algorithm (GA) for multiple-input multiple-output (MIMO) nonlinear systems. Individual SVM is used to approximate each output of the controlled plant. Then the model is used in MPC control scheme to predict the outputs of the controlled plant. The optimal control sequence is calculated using GA with elite preserve strategy. Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Key words: Support vector machine, Genetic algorithm, Nonlinear model predictive control, Neural network, Modeling

Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2048-2052.

Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm[J]. Chin.J.Chem.Eng., 2015, 23(12): 2048-2052.

参考文献

[1] S.J. Qin, T.A. Badgwell, A survey of industrial model predictive control technology, Control. Eng. Pract. 11 (7) (2003) 733-764.

[2] J. Richalet, Industrial applications of model based predictive control, Automatica 29 (5) (1993) 1251-1274.

[3] G.Wang,W. Yan, S. Chen, X. Zhang, H. Shao,Multi-model predictive control of ultrasupercritical coal-fired power unit, Chin. J. Chem. Eng. 22 (7) (2014) 782-787.

[4] C.R. Cutler, B.L. Ramaker, Dynamic matrix control - A computer control algorithm, Proceedings of Joint Automatic Control Conference, Piscataway, USA, 1980.

[5] R.M.C. De Keyser, A.R. Van Cauwenberghe, Extended prediction self-adaptive control, IFAC Symposium on Identification and System Parameter Estimation, 1985.

[6] R. Soeterboek, Predictive Control: A Unified Approach, Prentice-Hall, New York, 1992.

[7] R.K. Al Seyab, Y. Cao, Nonlinear model predictive control for the ALSTOM gasifier, J. Process Control 16 (8) (2006) 795-808.

[8] M.A. Henson, Nonlinear model predictive control: Current status and future directions, Comput. Chem. Eng. 23 (2) (1998) 187-202.

[9] Z.Y. Zou, M. Yu, Z.Z. Wang, X.H. Liu, Y.Q. Guo, F.B. Zhang, N. Guo, Nonlinear model algorithmic control of a pH neutralization process, Chin. J. Chem. Eng. 21 (4) (2013) 395-400.

[10] B.R. Maner, F.J. Doyle, B.A. Ogunnaike, R.K. Pearson, Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models, Automatica 32 (9) (1996) 1285-1301.

[11] A.L. Cervantes, O.E. Agamennoni, J.L. Figueroa, A nonlinear model predictive control system based on Wiener piecewise linear models, J. Process Control 13 (7) (2003) 655-666.

[12] K.S. Narendra, K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks 1 (1) (1990) 4-27.

[13] C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. 20 (3) (1995) 273-297.

[14] V.N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, 1998.

[15] P. Chen, Y.Z. Lu, Nonlinear model predictive control with the integration of Support Vector Machine and Extremal Optimization, Proceedings of 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, China, 2010.

[16] S. Iplikci, A support vector machine based control application to the experimental three-tank system, ISA Trans. 49 (3) (2010) 376-386.

[17] J.A.K. Suykens, Support vector machines: A nonlinear modelling and control perspective, Eur. J. Control. 7 (2) (2001) 311-327.

[18] V.N. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 2000.

[19] W. Karush, Minima of Functions of Several Variables With Inequalities as Side Constraints(Master's Thesis) Univ. of Chicago, USA, 1939.

[20] H.W. Kuhn, A.W. Tucker, Nonlinear programming, Proceedings of 2nd Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, USA, 1951.

[21] E.F. Camacho, C. Bordons,Model Predictive Control, Springer, Berlin, Germany, 2004.

[22] C.X. Mu, R.M. Zhang, C.Y. Sun, LS-SVMpredictive control based on PSO for nonlinear systems, Control Theory Appl. 27 (2) (2010) 164-168.

编辑推荐 0

Metrics

阅读次数

全文

184

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	184

来源	本网站	其他网站

次数	80	104
比例	43%	57%

摘要

2165

最新录用	在线预览	正式出版

0	0	2165

来源	本网站	其他网站

次数	117	2048
比例	5%	95%

Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

Nonlinearmodel predictive control based on support vectormachine and genetic algorithm

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 0

Metrics

本文评价

[1]	Jindong Dai, Chi Zhai, Jiali Ai, Guangren Yu, Haichao Lv, Wei Sun, Yongzhong Liu. A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation[J]. 中国化学工程学报, 2023, 60(8): 262-274.
[2]	Borui Liu, Tao Zhang, Yi Zheng, Kailong Li, Hui Pan, Hao Ling. A dynamic control structure of liquid-only transfer stream distillation column[J]. 中国化学工程学报, 2023, 59(7): 135-145.
[3]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm[J]. 中国化学工程学报, 2023, 58(6): 244-255.
[4]	Guangyuan Chen, Tong Zhou, Meng Zhang, Zhongxiang Ding, Zhikun Zhou, Yuanhui Ji, Haiying Tang, Changsong Wang. Effects of heavy metal ions Cu²⁺/Pb²⁺/Zn²⁺ on kinetic rate constants of struvite crystallization[J]. 中国化学工程学报, 2023, 57(5): 10-16.
[5]	Shanwei Xiong, Li Zhou, Yiyang Dai, Xu Ji. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. 中国化学工程学报, 2023, 56(4): 1-14.
[6]	Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao. Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration[J]. 中国化学工程学报, 2023, 56(4): 46-57.
[7]	Bowen Jiang, Jia Liu, Guoqiang Yang, Zhibing Zhang. Efficient conversion of CO₂ into cyclic carbonates under atmospheric by halogen and metal-free poly(ionic liquid)s[J]. 中国化学工程学报, 2023, 55(3): 202-211.
[8]	Hany M. Abd El-Lateef, Mai M. Khalaf, K. Shalabi, Antar A. Abdelhamid. Multicomponent synthesis and designing of tetrasubstituted imidazole compounds catalyzed via ionic-liquid for acid steel corrosion protection: Experimental exploration and theoretical calculations[J]. 中国化学工程学报, 2023, 55(3): 304-319.
[9]	Kun Ren, Zheng Jiao, Xiaolong Wu, Honggui Han. Multivariable identification of membrane fouling based on compacted cascade neural network[J]. 中国化学工程学报, 2023, 53(1): 37-45.
[10]	Xibao Zhang, Zhenghong Luo. Bubble size modeling approach for the simulation of bubble columns[J]. 中国化学工程学报, 2023, 53(1): 194-200.
[11]	Zhi-Guo Yuan, Yu-Xia Wang, You-Zhi Liu, Dan Wang, Wei-Zhou Jiao, Peng-Fei Liang. Research and development of advanced structured packing in a rotating packed bed[J]. 中国化学工程学报, 2022, 49(9): 178-186.
[12]	Denglong Ma, Ruitao Wu, Zekang Li, Kang Cen, Jianmin Gao, Zaoxiao Zhang. A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model[J]. 中国化学工程学报, 2022, 48(8): 166-175.
[13]	Kangcheng Wang, Jie Zhang, Dexian Huang. Online temperature estimation of Shell coal gasification process based on extended Kalman filter[J]. 中国化学工程学报, 2022, 47(7): 134-144.
[14]	Tong Qin, Zhenhao Xi, Ling Zhao, Weikang Yuan. Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes[J]. 中国化学工程学报, 2022, 46(6): 231-242.
[15]	Xiang Wu, Yuzhou Hou, Kanjian Zhang, Ming Cheng. Dynamic optimization of 1,3-propanediol fermentation process: A switched dynamical system approach[J]. 中国化学工程学报, 2022, 44(4): 192-204.