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J4  2012, Vol. 46 Issue (8): 1506-1511    DOI: 10.3785/j.issn.1008-973X.2012.08.023
电气工程     
预粉磨系统的智能建模与复合控制
刘志鹏, 颜文俊
浙江大学 电气工程学院,浙江 杭州 310027
Intelligent modeling and compound control of pre-grinding system
LIU Zhi-peng, YAN Wen-jun
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
 全文: PDF 
摘要:

为了提高水泥生产过程中预粉磨系统的运行效率,减少水泥生产过程的能源消耗.针对预粉磨系统的特点,对预粉磨系统进行深入分析,设计一种智能建模与复合控制系统来稳定和优化预粉磨系统的运行状况.将预粉磨系统分为2个不同的子系统,一个子系统通过模糊逻辑控制器(FLC)进行控制,另一个子系统通过基于线性矩阵不等式的模型预测控制方法(LMI-based MPC)进行控制,预测模型则是通过最小二乘支持向量机(LS-SVM)获得.最后通过OPC,DCS和C++等相关软件技术的支持实现了控制系统,并在工业现场应用,对预粉磨系统的运行进行控制.结果表明,控制系统可实现对预粉磨系统的优化控制,预粉磨系统运行的稳定性有了很大的提高.

关键词: 预粉磨系统模糊逻辑控制最小二乘支持向量机线性矩阵不等式模型预测控制    
Abstract:

In order to improve the operational efficiency and energy saving in cement plants. Detailed analysis on the pre-grinding system according its features was given. An intelligent modeling and compoud control system was built to stablize and optimize the pre-grinding system in this paper. The whole system was devided into two sub-systems, one is controlled by fuzzy logic control (FLC), and the other was handled by linear matrix inequality based model predictive control (LMI-based MPC), and the model was acquired by using least square support vector machine (LS-SVM) regression. The control system was implanted with the support of OPC, DCS and C++ techniques. The control system has been deployed in the field to control the pre-grinding system. The result shows that the pre-grinding system gets a good control from the control system, and pre-grinding system is more stable than what it was before.

Key words: pre-grinding system    FLC    LS-SVM    LMI    MPC
出版日期: 2012-09-03
:  TP 273  
基金资助:

国家自然科学基金资助项目(60574079);浙江省自然科学基金资助项目(601112).

通讯作者: 颜文俊,男,教授,博导.     E-mail: yanwenjun_zju@163.com
作者简介: 刘志鹏(1988—),男,硕士生,从事系统优化与控制研究.E-mail:lzp@zju.edu.cn
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引用本文:

刘志鹏, 颜文俊. 预粉磨系统的智能建模与复合控制[J]. J4, 2012, 46(8): 1506-1511.

LIU Zhi-peng, YAN Wen-jun. Intelligent modeling and compound control of pre-grinding system. J4, 2012, 46(8): 1506-1511.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2012.08.023        http://www.zjujournals.com/xueshu/eng/CN/Y2012/V46/I8/1506

[1] 邹伟斌,邹捷.预粉磨技术及其应用要点分析[J].水泥科技,2008, (4): 17-21.
ZOU Weibin, ZOU Jie. Pregrinding technology and analysis of its application key points[J]. Science and Technology of Cement, 2008,( 4): 17-21.
[2] DU Xufeng, CHENG Qimin, LU Wenying. Hybrid fuzzy PID decoupling control using in ball mill [C]∥ International Conference on Sustainable Power Generation and Supply. Portugal: [s. n.], 2009: 2494-2498.
[3] MAGNI L, BASTIN G, WERTZ V. Multivariable nonlinear predictive control of cement mills[C]∥ IEEE Transactions on Control Systems Technology. Portugal: [s. n.], 1999: 502-508.
[4] XIE HB, JIANG ZY, LIU XH, et al. Application of fuzzy control of laminar cooling for hot rolled strip[J]. Journal of Materials Processing Technology, 2007,187: 715-719.
[5] SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Process Lett, 1999, 3(9): 293-300.
[6] 林伟青,傅建中,许亚洲,等.基于最小二乘支持向量机的数控机床热误差预测[J].浙江大学学报:工学版,2008, 42(6): 905-908.
LIN Weiqing, FU Jianzhong, XU Yazhou,, et al. Thermal error prediction of numerical control machine tools based on least squares support vector machines [J]. Journal of Zhejiang University: Engineering Science, 2008, 42(6): 905-908.
[7] 蒋静坪.计算机实时控制[M].杭州:浙江大学出版社,1992: 55-120.
[8] SOUZA C D, TROFINO A. An LMI approach to stablization of linear discretetime periodic systems [J]. International Journal of Control, 2000, 73(8): 696-703.
[9] KOTHARE M, BALAKRISHNAN V, MORARI M. Robust constrained model predictive control using linear matrix inequalities[J]. Automatica, 1996, 32(10): 1361-1379.
[10] 冯纯伯,田玉平,忻欣.鲁棒控制系统设计[M].南京:东南大学出版社, 1995: 21-73.

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