灰色模糊PID算法在煤泥水絮凝沉降过程控制中的应用研究
|
摘要
煤泥水处理是选煤厂非常重要的工艺环节,其处理效果直接影响洗水复用与闭路循环的指标,而且对选煤厂其他环节如分选效率、产品的数质量指标等都影响很大,甚至是制约全厂经济效益和社会效益的一个重要系统。因此,如何处理好煤泥水一直是国内外选煤界非常重视的研究内容。在煤泥水处理过程中实现有效的絮凝沉降是保证循环水质量的重要条件,但目前国内仍有许多选煤厂主要靠人工调节絮凝剂的添加量,导致循环水浊度不稳定。部分选煤厂虽已实现药剂自动添加,但对于絮凝沉降过程的复杂性,尤其是具有大惯性、大滞后的特点,均未能给出针对性的控制策略。因此,研究先进的控制方法,改善控制效果,对选煤厂具有很重要的意义。
本文主要做了以下几方面的工作：
1、通过对煤泥水性质的测定,详细分析了煤泥水的特点和难处理的原因；研究了煤泥水的沉降特性和影响煤泥水沉降的各种因素；分析了煤泥水絮凝沉降过程这样一个复杂的受多因素影响的大惯性、大滞后和非线性过程目前在控制上存在的问题和难点,如目前采用的控制算法对大滞后问题考虑不足,被控对象难以用精确的数学模型描述等。
2、本文在前馈加反馈联合控制的控制模式下,对如何克服系统大滞后的影响,提高系统的稳定性进行了重点研究,首先提出了一种沿浓缩机深度方向的三点式浊度分布检测方法,用以替代在溢流总管上的浊度检测,以提前和直接反映沉降效果；同时采用灰色预测算法,根据现在和过去浊度数据及其变化行为,对溢流水未来的浊度值进行了超前预测,从而大大降低了过程滞后对控制性能造成的影响。
3、基于改进的灰色预测-模糊PID控制原理,提出了一种改进的煤泥水絮凝沉降过程前馈加反馈控制策略,其改进之处在于：(1)用模糊PID算法替代了目前采用的常规模糊控制算法,即根据模糊规则在线整定PID参数,既克服了模糊控制难以消除稳态误差和实现精确控制的弱点,又克服了PID参数整定的困难,使它们的结合形成优势互补；(2)用沉降区的浊度变化矢量代替了澄清区浊度偏差的微分作为模糊控制器的第二维输入,将浊度的变化趋势提前输入模糊控制器,对大滞后过程起到了提前控制的作用。该控制策略是一种简单、易于实现、而且鲁棒性强、控制有效的模式,适合于在环境恶劣的工业过程控制中应用。
4、本文仿真分析了灰色预测模型维数、原始数据序列的波动性以及预测步长对预测精度的影响,研究了PID参数的模糊推理规则,进行了论域分析和隶属度函数设计,并用实验方法建立了某种条件假设下的絮凝沉降过程的输入输出关系,通过系统辨识得到了煤泥水沉降过程的模拟模型。在此基础上进行了控制性能的仿真分析,结果表明灰色预测模糊PID控制对大滞后过程具有较好控制特性和抗干扰能力。
5、设计并实现了由现场显示与调节单元、下位机PLC控制器和远程上位机控制器组成的煤泥水絮凝沉降控制系统。借助上位机的运算能力应用MATLAB与组态软件的混合编程,实现灰色预测的过程；下位机PLC完成对系统传感器数据的采集和加药量的模糊控制。上、下位机通过工业以太网络交换数据和控制信息。
In coal preparation plant, slime water treatment system plays a very important role for the reason that the process water must be recycled. Generally, the turbidity of overflow water in thickening tank should below the required level. Unqualified recycled water will have impact on the production quotas such as separating effect, heavy medium consumption and product moisture. Especially, the severe disorder of slime water treatment system will result in anomaly, even shut down, of the overall coal separating system. Due to the great environmental concerns and potential economic reward, in the past decades many efforts have been made by both here and abroad researcher to study the methods of slime water treatment. However, most plants still rely on experienced workers'manual adjustment to control the addition of flocculant nowadays, which may lead to unstable water turbidity. In some factories, the automatic dosing has adopted, but the efficient control schemes to the great inertia and long delay of flocculating sedimentation process is lacking. Therefore, it is required to further study the advanced control methods for the specific problem.
In this paper, we make the following contributions:
1. We do some experiments on the samples of the coal slime water. From the parameters measured in the experiments, we analyze the properties of coal clime water and the multiple factors related to its sedimentation process. Based on such observations, we conclude that the particular challenges in effective control:large inertia and long latency. It is found that the existing schemes underestimate these problems.
2. To solve the system's large latency problem, we propose a novel turbidity detection method, which senses the turbidity at three locations in the vertical direction along the thickener. Compared with the traditional method only observing the turbidity at the overflow pipe, the new scheme is able to discover the sedimentation's situation with reduced delay. We develop grey prediction algorithm to foretell the turbidity in advance so that the delay problem is much alleviated.
3. Based on theories about grey prediction and fuzzy PID, we present a new flocculating sedimentation strategy with feed-forward and feedback. The scheme is different from the existing schemes in two ways. Firstly, the traditional fuzzy control method is substituted by the fuzzy PID algorithm, which can adjust the PID parameters in an online manner according to the fuzzy rules. The advantages are twofold:it could avoid the steady state error problem for fuzzy control methods; and it makes the PID parameter setting convenient. Secondly, the turbidity variation vector in the sinking region replaces the turbidity error differentials at clarity region as the second input for the fuzzy controller. Since the trend of turbidity change could be inferred, the large-delay system can be controlled in advance. Besides, the control strategy is practical to implement in harsh industrial environment.
4. Through extensive simulations, we study the impacts of a number of settings on the accuracy of prediction, like the grey prediction model dimensions, the fluctuation of data sequence, and prediction step size. Based on PID fuzzy deduction rules, we make domain analysis and design member functions. A virtual model of coal slime water sedimentation has been derived by experiments and system identification method, which discloses an input-output relation of sedimentation process. The results show that the proposed control method is effective for large-delay system, even with interference signals.
5. Finally, we design and implement the automatic control system of flocculating sedimentation process which is composed of the display and adjusting unit on-site, lower machine PLC and upper machine PC. With operation ability of PC, grey forecasting process is realized by Mingled-Programming between MATLAB and configuration software. System Sensor data acquisition and the dosage of the fuzzy control were done by PLC. Upper machine and lower machine can exchange data and control information through the industrial Ethernet network.
本文主要做了以下几方面的工作：
1、通过对煤泥水性质的测定,详细分析了煤泥水的特点和难处理的原因；研究了煤泥水的沉降特性和影响煤泥水沉降的各种因素；分析了煤泥水絮凝沉降过程这样一个复杂的受多因素影响的大惯性、大滞后和非线性过程目前在控制上存在的问题和难点,如目前采用的控制算法对大滞后问题考虑不足,被控对象难以用精确的数学模型描述等。
2、本文在前馈加反馈联合控制的控制模式下,对如何克服系统大滞后的影响,提高系统的稳定性进行了重点研究,首先提出了一种沿浓缩机深度方向的三点式浊度分布检测方法,用以替代在溢流总管上的浊度检测,以提前和直接反映沉降效果；同时采用灰色预测算法,根据现在和过去浊度数据及其变化行为,对溢流水未来的浊度值进行了超前预测,从而大大降低了过程滞后对控制性能造成的影响。
3、基于改进的灰色预测-模糊PID控制原理,提出了一种改进的煤泥水絮凝沉降过程前馈加反馈控制策略,其改进之处在于：(1)用模糊PID算法替代了目前采用的常规模糊控制算法,即根据模糊规则在线整定PID参数,既克服了模糊控制难以消除稳态误差和实现精确控制的弱点,又克服了PID参数整定的困难,使它们的结合形成优势互补；(2)用沉降区的浊度变化矢量代替了澄清区浊度偏差的微分作为模糊控制器的第二维输入,将浊度的变化趋势提前输入模糊控制器,对大滞后过程起到了提前控制的作用。该控制策略是一种简单、易于实现、而且鲁棒性强、控制有效的模式,适合于在环境恶劣的工业过程控制中应用。
4、本文仿真分析了灰色预测模型维数、原始数据序列的波动性以及预测步长对预测精度的影响,研究了PID参数的模糊推理规则,进行了论域分析和隶属度函数设计,并用实验方法建立了某种条件假设下的絮凝沉降过程的输入输出关系,通过系统辨识得到了煤泥水沉降过程的模拟模型。在此基础上进行了控制性能的仿真分析,结果表明灰色预测模糊PID控制对大滞后过程具有较好控制特性和抗干扰能力。
5、设计并实现了由现场显示与调节单元、下位机PLC控制器和远程上位机控制器组成的煤泥水絮凝沉降控制系统。借助上位机的运算能力应用MATLAB与组态软件的混合编程,实现灰色预测的过程；下位机PLC完成对系统传感器数据的采集和加药量的模糊控制。上、下位机通过工业以太网络交换数据和控制信息。
In coal preparation plant, slime water treatment system plays a very important role for the reason that the process water must be recycled. Generally, the turbidity of overflow water in thickening tank should below the required level. Unqualified recycled water will have impact on the production quotas such as separating effect, heavy medium consumption and product moisture. Especially, the severe disorder of slime water treatment system will result in anomaly, even shut down, of the overall coal separating system. Due to the great environmental concerns and potential economic reward, in the past decades many efforts have been made by both here and abroad researcher to study the methods of slime water treatment. However, most plants still rely on experienced workers'manual adjustment to control the addition of flocculant nowadays, which may lead to unstable water turbidity. In some factories, the automatic dosing has adopted, but the efficient control schemes to the great inertia and long delay of flocculating sedimentation process is lacking. Therefore, it is required to further study the advanced control methods for the specific problem.
In this paper, we make the following contributions:
1. We do some experiments on the samples of the coal slime water. From the parameters measured in the experiments, we analyze the properties of coal clime water and the multiple factors related to its sedimentation process. Based on such observations, we conclude that the particular challenges in effective control:large inertia and long latency. It is found that the existing schemes underestimate these problems.
2. To solve the system's large latency problem, we propose a novel turbidity detection method, which senses the turbidity at three locations in the vertical direction along the thickener. Compared with the traditional method only observing the turbidity at the overflow pipe, the new scheme is able to discover the sedimentation's situation with reduced delay. We develop grey prediction algorithm to foretell the turbidity in advance so that the delay problem is much alleviated.
3. Based on theories about grey prediction and fuzzy PID, we present a new flocculating sedimentation strategy with feed-forward and feedback. The scheme is different from the existing schemes in two ways. Firstly, the traditional fuzzy control method is substituted by the fuzzy PID algorithm, which can adjust the PID parameters in an online manner according to the fuzzy rules. The advantages are twofold:it could avoid the steady state error problem for fuzzy control methods; and it makes the PID parameter setting convenient. Secondly, the turbidity variation vector in the sinking region replaces the turbidity error differentials at clarity region as the second input for the fuzzy controller. Since the trend of turbidity change could be inferred, the large-delay system can be controlled in advance. Besides, the control strategy is practical to implement in harsh industrial environment.
4. Through extensive simulations, we study the impacts of a number of settings on the accuracy of prediction, like the grey prediction model dimensions, the fluctuation of data sequence, and prediction step size. Based on PID fuzzy deduction rules, we make domain analysis and design member functions. A virtual model of coal slime water sedimentation has been derived by experiments and system identification method, which discloses an input-output relation of sedimentation process. The results show that the proposed control method is effective for large-delay system, even with interference signals.
5. Finally, we design and implement the automatic control system of flocculating sedimentation process which is composed of the display and adjusting unit on-site, lower machine PLC and upper machine PC. With operation ability of PC, grey forecasting process is realized by Mingled-Programming between MATLAB and configuration software. System Sensor data acquisition and the dosage of the fuzzy control were done by PLC. Upper machine and lower machine can exchange data and control information through the industrial Ethernet network.
引文
1.国家能源局,煤炭工业发展“十二五”规划[N],中国新闻网,2012.3
2.高文永,单葆国.中国能源供需特点与能源结构调整[J].华北电力大学学报(社会科学版),2010,(5).
3.王阳,江汝峰,徐明珠等.中国21世纪能源发展趋势[J].黑龙江科技信息,2010,(22).
4.张东晨,张明旭,陈清如.煤泥水处理中絮凝剂的应用现状及发展展望[J].选煤技术,2004,4：1-3.
5.陈贵斌.我国矿山机械设备的发展需求分析[J].中国科技博览,2011,18.
6.张明旭,选煤厂煤泥水处理[M],徐州：中国矿业大学出版社.2005.10
7.宋振玲,杨奎奇,赵跃民.浅析煤泥水处理系统及洗水闭路循环[J].煤炭技术,2003,(10).
8. A.T Owen, P.D Fawell, J.D Swift. The impact of polyacrylamide flocculant solution age on flocculation performance [J]. International Journal of Mineral Processing,2002,67:123-144
9.陈红叶.云岗矿选煤厂煤泥水处理工艺浅析[JJ.选煤技术,2002,2：8-20.
10.童朝东,东滩煤矿选煤厂药剂添加自动控制系统[J],洁净煤技术：2001(01)
11.李小伟,王知学,马建辉等,选煤厂絮凝剂自动投加系统设计[J],科技创新导报：2011(02)
12.徐晖,自动配制和添加絮凝剂装置的研制[J],机械管理开发：2011(02)
13.张广军,孙新安,耙式浓缩机药剂添加自动控制系统的开发[J],洁净煤技术： 2007(01)
14.王整风,多台中煤浓缩机絮凝剂自动添加系统[J],工矿自动化：2009(08)
15.岳广礼,选煤厂浓缩机药剂自动添加控制系统的设计[J],煤炭加工与综合利用：2006(06)
16.柴晓敏,絮凝剂自动加药系统的设计与变频器的应用[J],煤质技术：2004 (02)
17.董宪姝,高贵军,寇子明,基于煤泥界面检测的絮凝剂溶解液自动添加系统的研究,选煤技术：2007(04)
18.高贵军,絮凝剂溶解液制备及自动添加机理研究[D],太原理工大学博士论文,2010
19.王卫东,刘卫东,付苻存等,自动加药浓缩机超声检测技术,煤矿机电：2008 (02)
20.张超,基于PLC模糊控制的煤泥水自动加药系统的研究,安徽理工大学硕士学位论文：2008
21.高贵军,寇子明,选煤J一絮凝剂溶解液自动添加模糊控制系统的研究[J],矿山机械：2010(05)
22.吴新财,张明旭,王娜,PLC模糊控制器在煤泥水处理中的应用[J1,工矿自动化：2007(05)
23.王永初.滞后过程的预估与控制[M].北京：机械工业出版社,1987
24.沈平.时间滞后调节系统[M].北京：化工出版社,1985
25.李钟慎.滞后过程的高鲁棒性控制方法的研究[D].厦门：华侨大学,2007
26. Weidong Zhang, Xiaoming Xu. Analytical design and analysis of mismatched Smith predictor[J]. ISA Transactions,2001,40:133-138
27. Palmor Z J. Stability properties of Smith dead time compensator controller[J]. Int. J Contr. 1980,32(6):937-949
28. Watanabe K. A process-model control for linear system with delay [J]. IEEE Trans AC-26,1981,(6):1261-1269
29. Weidong Zhang, Xing He and Xiaoming Xu. Comparison of several well-known controllers used in process control[J]. ISA Transactions,2003,42(2):317-325
30. Dahlin E B. Designing and tuning digital controllers[J]. Instr. & Contr. Sys.,1968, 41(7):77-79
31.张汉祥.消除振铃现象的一种改进方法[J].自动化学报,1992,18(4)：508-512
32.张卫东,许晓鸣,瞿海斌.二阶对象Dahlin控制器振铃的消除[J].仪器仪表学报,2000,21(2)：118-120
33. Richalet J, Rault A. Model predictive heuristic control:Application to Industrial Process[J]. Automatica,1978,14(1),413-428.
34. Culter C. R, Ramaker B. L. Dynamic Matric Control-A Computer Control Algorithm, proc. JACC, San Fancisco, WP5-B 1980
35. Rouhani R, MehlaR. K. Model Algorithmie Control(MAC):Basie Theorytieal ProPerties[J]. Automatiea,1982,18(4):401-414.
36. DENG J L. Introduction to grey system theory[J]. The Journal of Grey System,1989, 1(1):25-41.
37.李斌,郭凤仪,贾巍等.灰色广义预测控制在锅炉汽包水位控制中的应用研究,计算机测量与控制.2010.18(2)：370-373
38.王伟,吴敏,曹卫华.基于组合灰色预测模型的焦炉火道温度模糊专家控制,控制与决策,2010(2)
39.肖聚亮,王国栋,阎祥安等.变步长灰色预测模糊控制研究与应用[J].天津大学学报,2007,(7)
40.崔坤林,张翼飞.时滞系统的经典控制与智能控制[J].微计算机信息,2004,20(6)：25-26
41. Stuart B. A brief history of automatic control [J].IEEE Control System,1996,16(3):17-25
42.吴可.模糊数学的产生、发展和应用[J].科技信息(科学教研),2007,(29)
43.孙云辉,王钊,肖威等.基于PLC的离心风机模糊控制系统设计与实现[J].制造业自动化,2011,(10).
44.熊和金,徐中华.灰色控制[M].北京：国防工业出版社,2005.9
45.李庆春.新型PID模糊控制器的结构分析及应用研究.长沙：中南大学,2010
46. W Li.Design of a hybrid fuzzy logic Proportional Plus conventional integral-derivative controller.IEEE Transactions on Fuzzy Systems,1998,6(4):449-463
47. Ilyas Eker, Yunis Torun.Fuzzy logic control to be conventional method. Energy Conversion and Management,2006,47(4):377-394
48.王春艳.模糊PID复合控制在异丙醇精馏过程中的应用[J].化工自动化及仪表,2011,(12).
49.李亚峰.高浓度洗煤废水处理与回用技术研究[D].沈阳：东北大学,2006.
50.谢广元等.选矿学[M].北京：中国矿业大学出版社,2005.
51.盖春燕.高泥化煤泥水特性与处理工艺研究[D].太原：太原理工大学,2006
52.田小鹏.几种水溶性高分子化合物对煤泥浮选和过滤的影响规律研究[D].太原：太原理工大学,2006.
53.邓聚龙.灰色控制系统[M].武汉：华中工学院出版社,1992.
54.邓聚龙.灰色系统基本方法[M].武汉：华中工业出版社,1987.
55. Chao-Chin Chung, Ho-Hsien Chen, Ching-Hua Ting. Grey prediction fuzzy control for pH processes in the food industry [J]. Journal of Food Engineering 96 (2010) 575-582.
56. Che-Chiang Hsu, Chia-Yon Chen. Applications of improved grey prediction model for power demand forecasting[J]. Energy Conversion and Management,2003, 44(2241-2249).
57.沈继红.灰色系统理论预测方法研究及其在舰船运动预报中的应用[D],哈尔滨：哈尔滨工业大学,2001
58.王正方,王勇,刘秀华.基于灰色系统理论的常压蒸馏装置腐蚀预测[J].中国石油大学学报(自然科学出版社),2010,34(2)：114-120.
59.[20]方志,周光伟.大跨度连续刚构桥梁施工预测控制系统[J].中外公路,2003,23(4)：1-6.
60.刘思峰.灰色系统理论及应用[M].北京：科学出版社,2004.11
61.刘思峰.灰色系统理论的产生与发展[J].南京航空航天大学学报,2004,(2).
62.马志芳.基于灰色模型的预测模糊控制的研究[D].太原：太原理工大学,2006
63.姚向东,张立军.灰色预测控制的设计及其应用[J].电子与自动化,1998,Vo1.(4)：14-17
64. Thananchai Leephakpreeda. Grey prediction on indoor comfort temperature for HVAC systems[J].Expert Systems with Applications.2008(34):2284-2289
65.毕效辉,黄继起,姚琼荟.纯滞后系统的灰色预测控制[J].四川建材学院学报.1993,8(3)
66.陈孝伟.灰色预测模型的研究及其在汽温控制中的应用[D].北京：华北电力大学2008.12
67.李广义,黄景涛,田韶超.感应炉温度模糊PID控制系统的研究[J].电源技术,2012,(2).
68.韩沛,朱战霞,马卫华.基于模糊自适应PID的随动系统设计与仿真[J].计一算机仿真,2012,(1).
69. Tzuu-Hseng S. Li, Ming-Yuan Shieh.Design of a GA-based fuzzy PID controller for non-minimum phase systems[J]. Fuzzy Sets and Systems,2000,(111):183-197
70. Hsuan-Ming Feng and Ching-Chang Wong. An On-line Rule Tuning Grey Prediction Fuzzy Control Systems Design. Proc. Of the IEEE International Conference on Fuzzy System,2002.1316-1321
71.高泽东,李建军,高教波,王军,解俊虎.模糊自整定PID算法在伺服控制中的应用研究[J].自动化仪表,2011,(10).
72. Han Pu, Liu Hongjun, Wang Na. Research of Grey Predictive Fuzzy Controller for Large Time Delay System.2005 International Conference on Machine Learning and Cybernetics,2005:505-509
73. Wang D F, Han P, Han W, et al. Typical Grey Predictive Control Methods and simulation studies.2003 International Conference on Machine Leaning and Cybernetics[C].Xi'an, China,2003:513-518
74.仇成群,刘成林,沈法华等.基于Matlab和模糊PID的汽车巡航控制系统设计[J].农业工程学报,2012,(6).
75.王平军,侯波.基于模糊PID的飞机防滑刹车系统动态仿真[J].计算机仿真,2012,(2).
76. Zadeh L A. Fuzzy sets in information and control[J].Automatrica,1965,8(3):338-353.
77.葛锁良,刘文慰.基于模糊控制的交流伺服系统的设计[JJ.东南大学学报(自然科学版),2003,(S1).
78.吴闯,刘力军,何雨虹.模糊控制在空调器中应用[J].辽宁工程技术大学学报,2003,(3).
79. Onur Karasakal, Mujde Guzelkaya, Ibrahim Eksin, Engin Yesil. An error-based on-line rule weight adjustment method for fuzzy PID controllers[J].Expert Systems with Applications,2011,(38):10124-10132
80. T.P. Blanchett, G.C. Kember, R. Dubay. PID gain scheduling using fuzzy logic [J]. 2000(39):317-325
81. Hao Ying. Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme[J].Information Sciences,2000, (123):281-293
82. Chian-Chuang Ding, King-Tan Lee, et al. Optimal design for power system dynamic stabilizer by grey predictive PID[A].IEEE ICIT'02, bangkd, THAILAND,2002:279-284
83.张志勇,文桂林,时变时滞系统的灰色预测非线性PID控制,系统仿真学报,2009(9)：
84. R. J. Lian, B. F. Linb, and J. H. Huang. A grey prediction fuzzy controller for constant cutting force in turning. International Journal of Machine Tools & Manufacture, 45:1047-1056,2005.
85. J. Causa, G. Karer, A. Nunez, D. Saez, I. Skrjanc, and B. Zupancic. Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor.Computers and Chemical Engineering,32:3254-3263,2008.
2.高文永,单葆国.中国能源供需特点与能源结构调整[J].华北电力大学学报(社会科学版),2010,(5).
3.王阳,江汝峰,徐明珠等.中国21世纪能源发展趋势[J].黑龙江科技信息,2010,(22).
4.张东晨,张明旭,陈清如.煤泥水处理中絮凝剂的应用现状及发展展望[J].选煤技术,2004,4：1-3.
5.陈贵斌.我国矿山机械设备的发展需求分析[J].中国科技博览,2011,18.
6.张明旭,选煤厂煤泥水处理[M],徐州：中国矿业大学出版社.2005.10
7.宋振玲,杨奎奇,赵跃民.浅析煤泥水处理系统及洗水闭路循环[J].煤炭技术,2003,(10).
8. A.T Owen, P.D Fawell, J.D Swift. The impact of polyacrylamide flocculant solution age on flocculation performance [J]. International Journal of Mineral Processing,2002,67:123-144
9.陈红叶.云岗矿选煤厂煤泥水处理工艺浅析[JJ.选煤技术,2002,2：8-20.
10.童朝东,东滩煤矿选煤厂药剂添加自动控制系统[J],洁净煤技术：2001(01)
11.李小伟,王知学,马建辉等,选煤厂絮凝剂自动投加系统设计[J],科技创新导报：2011(02)
12.徐晖,自动配制和添加絮凝剂装置的研制[J],机械管理开发：2011(02)
13.张广军,孙新安,耙式浓缩机药剂添加自动控制系统的开发[J],洁净煤技术： 2007(01)
14.王整风,多台中煤浓缩机絮凝剂自动添加系统[J],工矿自动化：2009(08)
15.岳广礼,选煤厂浓缩机药剂自动添加控制系统的设计[J],煤炭加工与综合利用：2006(06)
16.柴晓敏,絮凝剂自动加药系统的设计与变频器的应用[J],煤质技术：2004 (02)
17.董宪姝,高贵军,寇子明,基于煤泥界面检测的絮凝剂溶解液自动添加系统的研究,选煤技术：2007(04)
18.高贵军,絮凝剂溶解液制备及自动添加机理研究[D],太原理工大学博士论文,2010
19.王卫东,刘卫东,付苻存等,自动加药浓缩机超声检测技术,煤矿机电：2008 (02)
20.张超,基于PLC模糊控制的煤泥水自动加药系统的研究,安徽理工大学硕士学位论文：2008
21.高贵军,寇子明,选煤J一絮凝剂溶解液自动添加模糊控制系统的研究[J],矿山机械：2010(05)
22.吴新财,张明旭,王娜,PLC模糊控制器在煤泥水处理中的应用[J1,工矿自动化：2007(05)
23.王永初.滞后过程的预估与控制[M].北京：机械工业出版社,1987
24.沈平.时间滞后调节系统[M].北京：化工出版社,1985
25.李钟慎.滞后过程的高鲁棒性控制方法的研究[D].厦门：华侨大学,2007
26. Weidong Zhang, Xiaoming Xu. Analytical design and analysis of mismatched Smith predictor[J]. ISA Transactions,2001,40:133-138
27. Palmor Z J. Stability properties of Smith dead time compensator controller[J]. Int. J Contr. 1980,32(6):937-949
28. Watanabe K. A process-model control for linear system with delay [J]. IEEE Trans AC-26,1981,(6):1261-1269
29. Weidong Zhang, Xing He and Xiaoming Xu. Comparison of several well-known controllers used in process control[J]. ISA Transactions,2003,42(2):317-325
30. Dahlin E B. Designing and tuning digital controllers[J]. Instr. & Contr. Sys.,1968, 41(7):77-79
31.张汉祥.消除振铃现象的一种改进方法[J].自动化学报,1992,18(4)：508-512
32.张卫东,许晓鸣,瞿海斌.二阶对象Dahlin控制器振铃的消除[J].仪器仪表学报,2000,21(2)：118-120
33. Richalet J, Rault A. Model predictive heuristic control:Application to Industrial Process[J]. Automatica,1978,14(1),413-428.
34. Culter C. R, Ramaker B. L. Dynamic Matric Control-A Computer Control Algorithm, proc. JACC, San Fancisco, WP5-B 1980
35. Rouhani R, MehlaR. K. Model Algorithmie Control(MAC):Basie Theorytieal ProPerties[J]. Automatiea,1982,18(4):401-414.
36. DENG J L. Introduction to grey system theory[J]. The Journal of Grey System,1989, 1(1):25-41.
37.李斌,郭凤仪,贾巍等.灰色广义预测控制在锅炉汽包水位控制中的应用研究,计算机测量与控制.2010.18(2)：370-373
38.王伟,吴敏,曹卫华.基于组合灰色预测模型的焦炉火道温度模糊专家控制,控制与决策,2010(2)
39.肖聚亮,王国栋,阎祥安等.变步长灰色预测模糊控制研究与应用[J].天津大学学报,2007,(7)
40.崔坤林,张翼飞.时滞系统的经典控制与智能控制[J].微计算机信息,2004,20(6)：25-26
41. Stuart B. A brief history of automatic control [J].IEEE Control System,1996,16(3):17-25
42.吴可.模糊数学的产生、发展和应用[J].科技信息(科学教研),2007,(29)
43.孙云辉,王钊,肖威等.基于PLC的离心风机模糊控制系统设计与实现[J].制造业自动化,2011,(10).
44.熊和金,徐中华.灰色控制[M].北京：国防工业出版社,2005.9
45.李庆春.新型PID模糊控制器的结构分析及应用研究.长沙：中南大学,2010
46. W Li.Design of a hybrid fuzzy logic Proportional Plus conventional integral-derivative controller.IEEE Transactions on Fuzzy Systems,1998,6(4):449-463
47. Ilyas Eker, Yunis Torun.Fuzzy logic control to be conventional method. Energy Conversion and Management,2006,47(4):377-394
48.王春艳.模糊PID复合控制在异丙醇精馏过程中的应用[J].化工自动化及仪表,2011,(12).
49.李亚峰.高浓度洗煤废水处理与回用技术研究[D].沈阳：东北大学,2006.
50.谢广元等.选矿学[M].北京：中国矿业大学出版社,2005.
51.盖春燕.高泥化煤泥水特性与处理工艺研究[D].太原：太原理工大学,2006
52.田小鹏.几种水溶性高分子化合物对煤泥浮选和过滤的影响规律研究[D].太原：太原理工大学,2006.
53.邓聚龙.灰色控制系统[M].武汉：华中工学院出版社,1992.
54.邓聚龙.灰色系统基本方法[M].武汉：华中工业出版社,1987.
55. Chao-Chin Chung, Ho-Hsien Chen, Ching-Hua Ting. Grey prediction fuzzy control for pH processes in the food industry [J]. Journal of Food Engineering 96 (2010) 575-582.
56. Che-Chiang Hsu, Chia-Yon Chen. Applications of improved grey prediction model for power demand forecasting[J]. Energy Conversion and Management,2003, 44(2241-2249).
57.沈继红.灰色系统理论预测方法研究及其在舰船运动预报中的应用[D],哈尔滨：哈尔滨工业大学,2001
58.王正方,王勇,刘秀华.基于灰色系统理论的常压蒸馏装置腐蚀预测[J].中国石油大学学报(自然科学出版社),2010,34(2)：114-120.
59.[20]方志,周光伟.大跨度连续刚构桥梁施工预测控制系统[J].中外公路,2003,23(4)：1-6.
60.刘思峰.灰色系统理论及应用[M].北京：科学出版社,2004.11
61.刘思峰.灰色系统理论的产生与发展[J].南京航空航天大学学报,2004,(2).
62.马志芳.基于灰色模型的预测模糊控制的研究[D].太原：太原理工大学,2006
63.姚向东,张立军.灰色预测控制的设计及其应用[J].电子与自动化,1998,Vo1.(4)：14-17
64. Thananchai Leephakpreeda. Grey prediction on indoor comfort temperature for HVAC systems[J].Expert Systems with Applications.2008(34):2284-2289
65.毕效辉,黄继起,姚琼荟.纯滞后系统的灰色预测控制[J].四川建材学院学报.1993,8(3)
66.陈孝伟.灰色预测模型的研究及其在汽温控制中的应用[D].北京：华北电力大学2008.12
67.李广义,黄景涛,田韶超.感应炉温度模糊PID控制系统的研究[J].电源技术,2012,(2).
68.韩沛,朱战霞,马卫华.基于模糊自适应PID的随动系统设计与仿真[J].计一算机仿真,2012,(1).
69. Tzuu-Hseng S. Li, Ming-Yuan Shieh.Design of a GA-based fuzzy PID controller for non-minimum phase systems[J]. Fuzzy Sets and Systems,2000,(111):183-197
70. Hsuan-Ming Feng and Ching-Chang Wong. An On-line Rule Tuning Grey Prediction Fuzzy Control Systems Design. Proc. Of the IEEE International Conference on Fuzzy System,2002.1316-1321
71.高泽东,李建军,高教波,王军,解俊虎.模糊自整定PID算法在伺服控制中的应用研究[J].自动化仪表,2011,(10).
72. Han Pu, Liu Hongjun, Wang Na. Research of Grey Predictive Fuzzy Controller for Large Time Delay System.2005 International Conference on Machine Learning and Cybernetics,2005:505-509
73. Wang D F, Han P, Han W, et al. Typical Grey Predictive Control Methods and simulation studies.2003 International Conference on Machine Leaning and Cybernetics[C].Xi'an, China,2003:513-518
74.仇成群,刘成林,沈法华等.基于Matlab和模糊PID的汽车巡航控制系统设计[J].农业工程学报,2012,(6).
75.王平军,侯波.基于模糊PID的飞机防滑刹车系统动态仿真[J].计算机仿真,2012,(2).
76. Zadeh L A. Fuzzy sets in information and control[J].Automatrica,1965,8(3):338-353.
77.葛锁良,刘文慰.基于模糊控制的交流伺服系统的设计[JJ.东南大学学报(自然科学版),2003,(S1).
78.吴闯,刘力军,何雨虹.模糊控制在空调器中应用[J].辽宁工程技术大学学报,2003,(3).
79. Onur Karasakal, Mujde Guzelkaya, Ibrahim Eksin, Engin Yesil. An error-based on-line rule weight adjustment method for fuzzy PID controllers[J].Expert Systems with Applications,2011,(38):10124-10132
80. T.P. Blanchett, G.C. Kember, R. Dubay. PID gain scheduling using fuzzy logic [J]. 2000(39):317-325
81. Hao Ying. Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme[J].Information Sciences,2000, (123):281-293
82. Chian-Chuang Ding, King-Tan Lee, et al. Optimal design for power system dynamic stabilizer by grey predictive PID[A].IEEE ICIT'02, bangkd, THAILAND,2002:279-284
83.张志勇,文桂林,时变时滞系统的灰色预测非线性PID控制,系统仿真学报,2009(9)：
84. R. J. Lian, B. F. Linb, and J. H. Huang. A grey prediction fuzzy controller for constant cutting force in turning. International Journal of Machine Tools & Manufacture, 45:1047-1056,2005.
85. J. Causa, G. Karer, A. Nunez, D. Saez, I. Skrjanc, and B. Zupancic. Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor.Computers and Chemical Engineering,32:3254-3263,2008.