基于自适应动态规划的矿渣微粉生产过程跟踪控制

王康; 李晓理; 贾超; 宋桂芝

doi:10.16383/j.aas.2016.c150808

基于自适应动态规划的矿渣微粉生产过程跟踪控制

doi: 10.16383/j.aas.2016.c150808

王康^1,,
李晓理^2, ,,
贾超^1,,
宋桂芝^3,

1.
北京科技大学自动化学院北京 100083
2.
北京工业大学电子信息与控制工程学院北京 100124
3.
济南鲁新新型建材股份有限公司济南 250109

基金项目:

国家自然科学基金 61673053

高等学校博士学科点专项科研基金 20130006110008

国家自然科学基金 61473034

详细信息

作者简介:
王康   北京科技大学自动化学院博士研究生.2012年获得北京科技大学自动化系学士学位.主要研究方向为最优控制, 自适应控制.E-mail:wangkangustb@gmail.com

贾超  北京科技大学自动化学院博士研究生.2011年获得青岛理工大学学士学位.主要研究方向为多模型控制, 模糊控制和神经网络控制.E-mail:jiachaocharles@outlook.com

宋桂芝  济南鲁新新型建材股份有限公司电气工程师.2007年获得山东大学电气工程及其自动化硕士学位.主要研究方向为大型立磨系统的自动控制.E-mail:luxinsonggz@163.com

通讯作者:
李晓理北京工业大学电子信息与控制工程学院教授.1997年获得大连理工大学控制理论与工程硕士学位, 2000年获得东北大学博士学位.主要研究方向为多模型自适应控制, 神经网络控制.本文通信作者.E-mail:lixiaolibjut@bjut.edu.cn

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出版历程
- 收稿日期: 2015-11-30
- 录用日期: 2016-03-02
- 刊出日期: 2016-10-20

Optimal Tracking Control for Slag Grinding Process Based on Adaptive Dynamic Programming

WANG Kang^1
,,
LI Xiao-Li^{2
, ,},
JIA Chao^1
,,
SONG Gui-Zhi^3
,

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083
2.
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124
3.
Jinan Luxin Materials Company Limited, Jinan 250109

Funds:

National Natural Science Foundation of China 61673053

Specialized Research Fund for the Doctoral Program of Higher Education 20130006110008

National Natural Science Foundation of China 61473034

More Information

Author Bio:
Ph. D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his bachelor degree from University of Science and Technology Beijing in 2012. His research interest covers optimal control and adaptive control.E-mail:

Ph. D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his bachelor degree from Qingdao Technological University in 2011. His research interest covers multiple model control, fuzzy control, and neural network control.E-mail:

Electrical engineer at Jinan Luxin Materials Company Limited. She received her bachelor degree in electric engineering and automation from Shandong University in 2007. Her research interest covers automatic control of large scale vertical mill.E-mail:

Corresponding author: LI Xiao-Li Professor at the College of Electronic Information and Control Engineering, Beijing University of Technology. He received his master degree in control theory and control engineering from Dalian University of Technology in 1997, and Ph. D. degree from Northeastern University in 2000, respectively. His research interest covers multiple model adaptive control and neural network control. Corresponding author of this paper.E-mail:lixiaolibjut@bjut.edu.cn

摘要

摘要: 矿渣微粉是一种新型绿色环保型建材，可以大大提高水泥混凝土的力学性能.本文以矿渣微粉生产过程为研究对象，针对该过程难以通过机理建模进行辨识和控制的特点，利用数据驱动的思想，建立矿渣微粉生产过程的递归神经网络模型.在此基础上，利用自适应动态规划，设计具有控制约束的跟踪控制器，并将其应用到矿渣微粉生产过程中.仿真分析表明，建立的数据驱动模型能够有效地辨识矿渣微粉生产过程，同时，本文提出的控制方法能够实现输入受限的微粉比表面积及磨内压差的最优跟踪控制.
- 矿渣微粉 /
- 数据驱动 /
- 自适应动态规划 /
- 最优跟踪控制 /
- 输入有界
Abstract: Super fine slag powder is a new kind of green environmental-friendly construction material, which can greatly improve the mechanical properties of cement concrete. However, the slag powder grinding process is hard to identify by a mechanism model. In this paper, a data-driven based recurrent neural network model is constructed utilizing the information measured from slag grinding system. Based on this model, an adaptive dynamic programming algorithm is proposed to realize the optimal tracking control with constrained control input. Further, this algorithm is applied to the slag grinding process. Simulation examples show that the data-based model can effectively identify the grinding process, and the control method can realize the optimal tracking control of specific surface area and mill differential pressure with control constraints.
- Slag grinding process /
- data driven /
- adaptive dynamic programming /
- optimal tracking control /
- input constrained

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图 1 矿渣微粉生产监控画面

Fig. 1 Monitor screen of slag grinding process

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图 2 矿渣微粉生产流程图

Fig. 2 Flow chart of slag grinding process

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图 3 微粉颗粒受力图

Fig. 3 Stress analysis of slag powder

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图 4 模型辨识曲线

Fig. 4 Curve of model identification

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图 5 模型辨识误差曲线

Fig. 5 Curve of model identification error

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图 6 评价网权值曲线

Fig. 6 Critic network weights

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图 7 执行网权值曲线

Fig. 7 Actor network weights

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图 8 受约束控制曲线

Fig. 8 Constrained control signal

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图 9 无约束控制曲线

Fig. 9 Control signal without constraints

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图 10 状态输出曲线

Fig. 10 Output state signal

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表 1 济钢鲁新建材3号矿渣微粉生产线生产运行数据

Table 1 Production data of Luxin mill line 3

编号	水渣进料 (10³ kg/Hr)	电机转速 (r/min)	进口风温 (℃)	入磨循环风阀开度 (%)	比表面积 (cm²/g)	磨内压差 (mbar)
1	85.60	1 250	230	65.13	438.5	27.60
2	84.81	1 160	229	69.50	426.3	28.13
3	84.77	1 240	235	66.17	430.7	26.97
⋮	⋮	⋮	⋮	⋮	⋮	⋮
323	99.63	1 049	242	60.59	438.5	24.65
324	100.42	1 050	243	60.53	426.3	24.94
325	101.20	1 051	248	60.62	433.9	25.00

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表 2 各控制变量容许变化范围

Table 2 Tolerance range of different variables

	水渣进料 (103 kg/Hr)	电机转速 (r/min)	进口风温 (℃)	入磨循环风阀开度 (%)
最大值	160	1 300	300	80
最小值	0	0	150	0

下载: 导出CSV

参考文献(19)

[1]	Işikdağ, Topču Ī B. The effect of ground granulated blast-furnace slag on properties of Horasan mortar. Construction and Building Materials, 2013, 40:448-454 doi: 10.1016/j.conbuildmat.2012.11.016
[2]	Zhang Y J, Zhang X. Grey correlation analysis between strength of slag cement and particle fractions of slag powder. Cement and Concrete Composites, 2007, 29(6):498-504 doi: 10.1016/j.cemconcomp.2007.02.004
[3]	陈远.大型立磨选粉机研究[硕士学位论文], 重庆大学, 中国, 2008. http://cdmd.cnki.com.cn/article/cdmd-10611-2009050041.htm Chen Yuan. Study on Separator of Large-scale Vertical Mill[Master dissertation], Chongqing University, China, 2008. http://cdmd.cnki.com.cn/article/cdmd-10611-2009050041.htm
[4]	Xu J X, Hou Z S. Notes on data-driven system approaches. Acta Automatica Sinica, 2009, 35(6):668-675 https://www.researchgate.net/profile/Zhong_Sheng_Hou/publication/245568563_Notes_on_Data-driven_System_Approaches/links/56cc290908aee3cee5433e86.pdf?inViewer=0&pdfJsDownload=0&origin=publication_detail
[5]	侯忠生, 许建新.数据驱动控制理论及方法的回顾和展望.自动化学报, 2009, 35(6):650-667 doi: 10.3724/SP.J.1004.2009.00650 Hou Zhong-Sheng, Xu Jian-Xin. On data-driven control theory:the state of the art and perspective. Acta Automatica Sinica, 2009, 35(6):650-667 doi: 10.3724/SP.J.1004.2009.00650
[6]	代伟, 柴天佑.数据驱动的复杂磨矿过程运行优化控制方法.自动化学报, 2014, 40(9):2005-2014 http://www.aas.net.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=18472 Dai Wei, Chai Tian-You. Data-driven optimal operational control of complex grinding processes. Acta Automatica Sinica, 2014, 40(9):2005-2014 http://www.aas.net.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=18472
[7]	颜文俊, 秦伟.水泥立磨流程的建模和控制优化.控制工程, 2012, 19(6):929-943 http://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201206001.htm Yan Wen-Jun, Qin Wei. Modeling and control optimization in cement vertical roller mill process. Control Engineering of China, 2012, 19(6):929-943 http://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201206001.htm
[8]	Cai X Y, Meng Q J, Luan W L. Soft sensor of vertical mill material layer based on LS-SVM. In:Proceedings of the 2013 International Conference on Measurement, Information, and Control (ICMIC). Harbin, China:IEEE, 2013. 22-25
[9]	Lin X F, Qian Z. Modeling of vertical mill raw meal grinding process and optimal setting of operating parameters based on wavelet neural network. In:Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN). Beijing, China:IEEE, 2014. 3015-3020
[10]	Umucu Y, Çağlar M F, Gündüz L, Bozkurt V, Deniz V. Modeling of grinding process by artificial neural network for calcite mineral. In:Proceedings of the 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA). Istanbul, Turkey:IEEE, 2011. 344-348
[11]	张化光, 张欣, 罗艳红, 杨王君}.自适应动态规划综述.自动化学报, 2013, 39(4):303-311 doi: 10.1016/S1874-1029(13)60031-2 Zhang Hua-Guang, Zhang Xin, Luo Yan-Hong, Yang Jun. An overview of research on adaptive dynamic programming. Acta Automatica Sinica, 2013, 39(4):303-311 doi: 10.1016/S1874-1029(13)60031-2
[12]	Xu X, Zuo L, Huang Z H. Reinforcement learning algorithms with function approximation:recent advances and applications. Information Sciences, 2014, 261:1-31 doi: 10.1016/j.ins.2013.08.037
[13]	Murray J J, Cox C J, Lendaris G G, Saeks R. Adaptive dynamic programming. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews, 2002, 32(2):140-153 doi: 10.1109/TSMCC.2002.801727
[14]	Wei Q L, Liu D R, Yang X. Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(4):866-879 doi: 10.1109/TNNLS.2015.2401334
[15]	Liu D R, Wei Q L. Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(3):621-634 doi: 10.1109/TNNLS.2013.2281663
[16]	Zhang H, Cui L, Zhang X, Luo Y H. Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method. IEEE Transactions on Neural Networks, 2011, 22(12):2226-2236 doi: 10.1109/TNN.2011.2168538
[17]	Modares H, Lewis F L, Naghibi-Sistani M B. Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems. Automatica, 2014, 50(1):193-202 doi: 10.1016/j.automatica.2013.09.043
[18]	Wei Q, Liu D. A novel iterative θ-adaptive dynamic programming for discrete-time nonlinear systems. IEEE Transactions on Automation Science and Engineering, 2014, 11(4):1176-1190 doi: 10.1109/TASE.2013.2280974
[19]	Qin C B, Zhang H G, Luo Y H. Adaptive optimal control for nonlinear discrete-time systems. In:Proceedings of the 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning. Singapore:IEEE, 2013. 13-18