基于LabVIEW的矿井排水自动监控系统的设计与实现
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摘要
矿井排水系统是保证煤矿生产稳定运行,保障工人生命安全的重要系统。采用人工操作的排水系统存在排水效率低、经济效益差、容易发生事故等弊端。而且矿井涌水具有严重非线性,无法建立精确数学模型。传统的PID控制、模糊控制无法起到良好的控制效果。
为解决上述问题,提出应用自适应神经模糊推理系统(ANFIS)对水泵进行自动控制的方法,通过其自组织、自学习、高精度、稳定性好的特点弥补当前控制方式的不足。采用LabVIEW与MATLAB混合编程的方法设计了基于ANFIS的矿井水泵控制器,将ANFIS控制器与避峰填谷原则以及轮流工作原则融合,提出了矿井排水系统综合控制策略。运用LabVIEW软件完成对整套矿井排水自动监控系统的设计。
仿真研究表明,在控制矿井排水系统方面,与PID控制、模糊控制相比,ANFIS控制的上升时间更短,无超调,控制更精确,控制性能更好。效益分析表明,本系统能够创造出显著的经济效益与社会效益,具有广泛的推广价值。
Mine drainage system is an important system that can guarantee stable operation of coal production, protect the safety of the workers. The manual operation water drainage system has low efficiency, low economic efficiency, prone to accidents and other defects. And mine inflow has severe nonlinear, can not establish accurate mathematical model. Traditional PID control, fuzzy control can not play a good control effect.
To address these problems, proposes the application of adaptive neuro-fuzzy inference system (ANFIS) approach for automatic control of pump. Through its self-organization, self-learning, high accuracy, good stability to make up for the shortcomings of the previous control.Mixed with LabVIEW and MATLAB programming designed ANFIS-based mine pump controller. Combining ANFIS controller and the principle of avoiding the peak and trending to the vale and the principles to work by turns principle, it puts forward the mine drainage system comprehensive control strategy. Complete automatic monitoring system for the design of mine drainage using LabVIEW software.
Simulation results show that in the automatic control system of mine drainage, compared with the PID control and fuzzy control,ANFIS control has a shorter rise time, no overshoot, more precise control and control performance is better. Benefit analysis shows that the system can create significant economic and social benefits, with a wide range of promotional value.
为解决上述问题,提出应用自适应神经模糊推理系统(ANFIS)对水泵进行自动控制的方法,通过其自组织、自学习、高精度、稳定性好的特点弥补当前控制方式的不足。采用LabVIEW与MATLAB混合编程的方法设计了基于ANFIS的矿井水泵控制器,将ANFIS控制器与避峰填谷原则以及轮流工作原则融合,提出了矿井排水系统综合控制策略。运用LabVIEW软件完成对整套矿井排水自动监控系统的设计。
仿真研究表明,在控制矿井排水系统方面,与PID控制、模糊控制相比,ANFIS控制的上升时间更短,无超调,控制更精确,控制性能更好。效益分析表明,本系统能够创造出显著的经济效益与社会效益,具有广泛的推广价值。
Mine drainage system is an important system that can guarantee stable operation of coal production, protect the safety of the workers. The manual operation water drainage system has low efficiency, low economic efficiency, prone to accidents and other defects. And mine inflow has severe nonlinear, can not establish accurate mathematical model. Traditional PID control, fuzzy control can not play a good control effect.
To address these problems, proposes the application of adaptive neuro-fuzzy inference system (ANFIS) approach for automatic control of pump. Through its self-organization, self-learning, high accuracy, good stability to make up for the shortcomings of the previous control.Mixed with LabVIEW and MATLAB programming designed ANFIS-based mine pump controller. Combining ANFIS controller and the principle of avoiding the peak and trending to the vale and the principles to work by turns principle, it puts forward the mine drainage system comprehensive control strategy. Complete automatic monitoring system for the design of mine drainage using LabVIEW software.
Simulation results show that in the automatic control system of mine drainage, compared with the PID control and fuzzy control,ANFIS control has a shorter rise time, no overshoot, more precise control and control performance is better. Benefit analysis shows that the system can create significant economic and social benefits, with a wide range of promotional value.
引文
[1] Zou Can,Wu Li.Study on measures for water prevention and control in Chengchao Iron Mine,China[J]. Engineering Sciences,2010,(3):85-89.
[2]徐建文,周晨,冯宇峰.浅谈矿井水的资源化技术[J].煤,2010,(8):59-60+96.
[3]郭崇宏.自动化监控系统在泵站中的应用[J].机电工程技术,2008,(5):79-81+115.
[4] M Samkaraiah Sr Dgm .Automatic generation of pump impeller geometry[M]. PRODUCT REVIEW World Pumps, Volume 2000, Issue 406,July 2000, Pages 28-30.
[5]谢波.我国泵站自动化设备运行现状及建议[J].中国水能及电气化,2009,(Z1):78-80.
[6]信权.浅析泵站自动化监控系统的管理[J].科技创新导报,2009,(5):250.
[7]陈导.泵站电气自动化的必要性和设计思路[J].中国高新技术企业,2009,(13):189-190.
[8]周元.自动化控制技术在泵站中的应用[J].科技资讯,2009,(31):85-86.
[9] A.P.Demin,G.Kh.Ismaiylov.Water Consumption and Drainage in the Volga Basin[J]. Water Resources,2003,30(3),333-346.
[10]徐守坤,刘丽莎,石林,马正华.一种矿井主排水智能监控系统的设计探讨[J].工矿自动化, 2010, (01):85-88.
[11]陈华.矿井排水设备选型优化设计方法[J].煤炭工程,2009,(8):22-23.
[12]侯立泉,刘杰辉.煤矿主排水系统的现代化改造[J],煤矿机械,2010,(6):163-164.
[13]李祥龙,吕海清,张滨辞,万军令.有色矿山主排水自动化系统研究应用[J].电气时代,2010(7):108-109.
[14]陈坚,李琪,许建中,李端明,储训.中国泵站工程现状及“十一五”期间泵站更新改造任务[J].水利水电科技进展,2008,(2):84-88.
[15]翟永刚,史志高.长距离水泵自动监控装置的研制与应用[J].煤炭科技,2010,(1):55-56.
[16]煤矿安全规程[Z].国家安全监督管理总局,国家煤矿安全监察局, 2005.
[17]上海嘉什水泵有限公司.水泵分类[EB/OL].中国,(2008-04-11)[2010-10-20]. http://www.xiubeng.cn/ns_detail.asp?id=500018&nowmenuid=500002&previd=500013
[18]张勃.浅论离心泵的完好标准[J].新疆化工,2008,(4):24-26.
[19] Seref Naci Engin, Janset Kuvulmaz, Vasfi Emre Omurlu.Modeling of acoupled industrial Tank System with ANFIS[J].Lecture Notes in Computer Science,2004, 2972:804-812p.
[20] Kara S,Okandan M.Atrial fibrillation classification with artificial neural networks[J].Pattern Recognition,2007(4),73-78.
[21] Xu Z B,Zhang R, Jing W F. When does online BP training converge[J].IEEE Transactions on Neural Networks,2009,20(10):1529-1539.
[22] Qin H, Yang S X. Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancellation for images[J]. Fuzzy Sets and Systems,2007,158(10): 1036-1063.
[23]张智星,孙春在,[日]水谷英二.神经-模糊和软计算[M].西安交通大学出版社,2000.
[24] Yao X.Evolving Artificial Neural Networks[J].Proceedings of the IEEE, 1999,87(9) 1423-1447.
[25] Baruch I S, Lopez Q B, Flores J M. A fuzzy-neural multi-model for nonlinear systems identification and control[J]. Fuzzy Sets and Systems,2008,159:2650-2667.
[26] Wei Wu,Naimin Zhang,Zhengxue Li,Long Li,Yan Liu.Convergence of gradient method with momentum for back-propagation neural networks[J].Journal of Computational Mathematics,2008,26(4):613-623.
[27]唐兵勇,路林吉,王文杰.模糊控制理论与应用技术[M].清华大学出版社,2002,9 ( 34 ):33-34.
[28]叶伟,基于MATLAB的模糊控制器设计与实现[J].机床与液压,2005:126-128.
[29] Xie W F, Rad A B. Fuzzy adaptive internal model control[J]. IEEE Trans. on Industrial Electronics, 2000,47(1):193-202.
[30]王禹林,熊振华,丁汉.LabVIEW与Matlab的无缝集成[J].计算机应用,2006,(3):696-702.
[31]吕飞龙.基于LABVIEW的虚拟仪器温度检测系统的设计[J].传感器与仪器仪表,2007: 270-272.
[32] Aniruddha Mitra,Sahana Sen.FFT ANALYSIS USING LABVIEW AS APART OF VIBRATION AND PREVENTIVE MAINTENANCE CLASS FORSENIOR LEVEL MET STUDENTS[J].2005 ASME International Mechanical Engineering Congress and Exposition (IMECE 2005),2005.
[33]杨乐平,李海涛,杨磊.LabVIEW程序设计与应用[M].北京:电子工业出版社,2005.
[34]方志远,鲁植雄.基于LabVIEW同步控制技术的数据采集平台设计[J].工业控制计算机,2007,04(20):48-49,51.
[35] National Instruments. National Instruments LabVIEW Basic[M]. USA: National Instruments Co.Ltd,2002:136-141.
[2]徐建文,周晨,冯宇峰.浅谈矿井水的资源化技术[J].煤,2010,(8):59-60+96.
[3]郭崇宏.自动化监控系统在泵站中的应用[J].机电工程技术,2008,(5):79-81+115.
[4] M Samkaraiah Sr Dgm .Automatic generation of pump impeller geometry[M]. PRODUCT REVIEW World Pumps, Volume 2000, Issue 406,July 2000, Pages 28-30.
[5]谢波.我国泵站自动化设备运行现状及建议[J].中国水能及电气化,2009,(Z1):78-80.
[6]信权.浅析泵站自动化监控系统的管理[J].科技创新导报,2009,(5):250.
[7]陈导.泵站电气自动化的必要性和设计思路[J].中国高新技术企业,2009,(13):189-190.
[8]周元.自动化控制技术在泵站中的应用[J].科技资讯,2009,(31):85-86.
[9] A.P.Demin,G.Kh.Ismaiylov.Water Consumption and Drainage in the Volga Basin[J]. Water Resources,2003,30(3),333-346.
[10]徐守坤,刘丽莎,石林,马正华.一种矿井主排水智能监控系统的设计探讨[J].工矿自动化, 2010, (01):85-88.
[11]陈华.矿井排水设备选型优化设计方法[J].煤炭工程,2009,(8):22-23.
[12]侯立泉,刘杰辉.煤矿主排水系统的现代化改造[J],煤矿机械,2010,(6):163-164.
[13]李祥龙,吕海清,张滨辞,万军令.有色矿山主排水自动化系统研究应用[J].电气时代,2010(7):108-109.
[14]陈坚,李琪,许建中,李端明,储训.中国泵站工程现状及“十一五”期间泵站更新改造任务[J].水利水电科技进展,2008,(2):84-88.
[15]翟永刚,史志高.长距离水泵自动监控装置的研制与应用[J].煤炭科技,2010,(1):55-56.
[16]煤矿安全规程[Z].国家安全监督管理总局,国家煤矿安全监察局, 2005.
[17]上海嘉什水泵有限公司.水泵分类[EB/OL].中国,(2008-04-11)[2010-10-20]. http://www.xiubeng.cn/ns_detail.asp?id=500018&nowmenuid=500002&previd=500013
[18]张勃.浅论离心泵的完好标准[J].新疆化工,2008,(4):24-26.
[19] Seref Naci Engin, Janset Kuvulmaz, Vasfi Emre Omurlu.Modeling of acoupled industrial Tank System with ANFIS[J].Lecture Notes in Computer Science,2004, 2972:804-812p.
[20] Kara S,Okandan M.Atrial fibrillation classification with artificial neural networks[J].Pattern Recognition,2007(4),73-78.
[21] Xu Z B,Zhang R, Jing W F. When does online BP training converge[J].IEEE Transactions on Neural Networks,2009,20(10):1529-1539.
[22] Qin H, Yang S X. Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancellation for images[J]. Fuzzy Sets and Systems,2007,158(10): 1036-1063.
[23]张智星,孙春在,[日]水谷英二.神经-模糊和软计算[M].西安交通大学出版社,2000.
[24] Yao X.Evolving Artificial Neural Networks[J].Proceedings of the IEEE, 1999,87(9) 1423-1447.
[25] Baruch I S, Lopez Q B, Flores J M. A fuzzy-neural multi-model for nonlinear systems identification and control[J]. Fuzzy Sets and Systems,2008,159:2650-2667.
[26] Wei Wu,Naimin Zhang,Zhengxue Li,Long Li,Yan Liu.Convergence of gradient method with momentum for back-propagation neural networks[J].Journal of Computational Mathematics,2008,26(4):613-623.
[27]唐兵勇,路林吉,王文杰.模糊控制理论与应用技术[M].清华大学出版社,2002,9 ( 34 ):33-34.
[28]叶伟,基于MATLAB的模糊控制器设计与实现[J].机床与液压,2005:126-128.
[29] Xie W F, Rad A B. Fuzzy adaptive internal model control[J]. IEEE Trans. on Industrial Electronics, 2000,47(1):193-202.
[30]王禹林,熊振华,丁汉.LabVIEW与Matlab的无缝集成[J].计算机应用,2006,(3):696-702.
[31]吕飞龙.基于LABVIEW的虚拟仪器温度检测系统的设计[J].传感器与仪器仪表,2007: 270-272.
[32] Aniruddha Mitra,Sahana Sen.FFT ANALYSIS USING LABVIEW AS APART OF VIBRATION AND PREVENTIVE MAINTENANCE CLASS FORSENIOR LEVEL MET STUDENTS[J].2005 ASME International Mechanical Engineering Congress and Exposition (IMECE 2005),2005.
[33]杨乐平,李海涛,杨磊.LabVIEW程序设计与应用[M].北京:电子工业出版社,2005.
[34]方志远,鲁植雄.基于LabVIEW同步控制技术的数据采集平台设计[J].工业控制计算机,2007,04(20):48-49,51.
[35] National Instruments. National Instruments LabVIEW Basic[M]. USA: National Instruments Co.Ltd,2002:136-141.