变流量城市集中供热分布式控制系统
|
摘要
集中供热是现代化城市的主要基础设施之一,安装热力表、实行分户计量已成为我国发展城市集中供热的必然趋势。采用计算机技术和先进的控制方法,对供热系统进行监测和控制,实施变流量运行,能够克服定流量供热系统存在的供热质量差、冷热不均、能源浪费、计费不合理、管理水平落后等缺点。针对变流量供热系统的时变性、耦合性和滞后性等特点,提出了换热站二次网温度控制和恒压点压力控制方案,并利用神经网络实现了二次网供、回水温度的解耦控制。
静态解耦方法简单、易实现,但解耦不彻底,解耦效果时好时坏,造成系统运行不稳定。根据二次网供、回水温度间的耦合关系,提出了静态解耦和基于对角递归神经网络(DRNN)辩识的神经网络PID解耦相结合的控制方案,该方案利用静态解耦减弱变量间的耦合强度,利用基于DRNN的神经网络PID实现动态解耦,有效地克服了二次网供、回水温度间的耦合,同时将DRNN网络作为在线辩识器,自动调整网络权值,实现PID控制器参数的在线调整,克服了常规PID控制器参数难以整定和自适应能力差的缺陷。基于上述理论分析,采用静态解耦、常规PID解耦和基于DRNN的神经网络PID解耦的控制方案,对所建立的换热器数学模型分别进行了MATLAB仿真,仿真结果表明,采用带静态解耦的神经网络PID解耦控制方案,实现了动态近似解耦,静态完全解耦,具有响应速度快、超调量小的品质。在MATLAB仿真基础上,利用PCS-B型过程控制系统实验装置进行了实验研究,实验结果表明,采用神经网络PID控制器,动态调节时间和稳态误差明显减小,获得更加满意的控制效果。
按照“集中管理、分散控制”的思想,设计了以工控机为上位机、PLC为下位机,组态软件MCGS为操作平台的城市集中供热监控系统。在MCGS环境下,完成了供热系统监控中心的主画面、神经网络PID控制画面、操作面板画面、数据显示和历史曲线显示等界面的设计。该方案的应用可以提高供热企业的管理水平和经济效益。
Central heating is one of the main infrastructures of modern cities. The installation of calorimeter, achieving household measurement, has become an inevitable trend of development for our urban central heating. Using computer technology and advanced control method, monitoring and controlling heating system, implementing variable flow operation, can overcome the shortcomings of poor quality, hot and cold inhomogeneous, wasted energy, unreasonable fees and management level backward in fixed flow heating system in past. Against(consider) the characteristics, of coupling, and lagging of variable flow heating system, secondary network temperature control programs and constant pressure point pressure control program are designed, and decoupling control is realized by using neural network.
Although the static decoupling method is simple, easy to realize, the decoupling can not be made thoroughly which can make the system unsteady. According to the coupling relationship of temperature of water supply and return, a decoupling method that static decoupling combines with PID decoupling based on diagonal recurrent neural network (DRNN) is proposed. In the method, the intensity of coupling is reduced by using static decoupling and dynamic decoupling is realized by using PID decoupling based on DRNN, to overcome coupling of temperature of secondary water supply and return. Meanwhile, consider the defect of that parameter regulation is difficult and bad adaptive capacity for conventional PID controller, neural network PID controller based on DRNN is designed. DRNN network is regarded as online identification and can be adjusted the right value of the network automatically, to realize PID controller parameter adjustment online. Based on analysis of theories above, adopting the methods of static decoupling, conventional PID decoupling and neural network PID decoupling, MATLAB simulations are carried for mathematical model of heat exchanger established. MATLAB simulation results shows that using neural network PID decoupling control programs with static decoupling has performed dynamic approximate decoupled and static completely decoupled, with the quality of fast response and small overshoot. Furthermore, Using PCS-B process control system experimental device, experimental study has been done. The results show that neural network PID controller can improve quality of dynamic process and reduce static error in practical application. And the satisfactory effect can be reached.
According to "centralized management, decentralized control" thought, urban central heating system adopts three-tier structure. And the program of heating station monitoring control system is designed, IPC is upper computer and PLC is subordinate computer based on MCGS configuration software platform in this program. In MCGS environment, main screen, neural network PID control screen, operation panel screen and data curve are designed for monitoring center in heating system. The application of this program would improve remarkably management level and economy benefit for heating enterprise.
静态解耦方法简单、易实现,但解耦不彻底,解耦效果时好时坏,造成系统运行不稳定。根据二次网供、回水温度间的耦合关系,提出了静态解耦和基于对角递归神经网络(DRNN)辩识的神经网络PID解耦相结合的控制方案,该方案利用静态解耦减弱变量间的耦合强度,利用基于DRNN的神经网络PID实现动态解耦,有效地克服了二次网供、回水温度间的耦合,同时将DRNN网络作为在线辩识器,自动调整网络权值,实现PID控制器参数的在线调整,克服了常规PID控制器参数难以整定和自适应能力差的缺陷。基于上述理论分析,采用静态解耦、常规PID解耦和基于DRNN的神经网络PID解耦的控制方案,对所建立的换热器数学模型分别进行了MATLAB仿真,仿真结果表明,采用带静态解耦的神经网络PID解耦控制方案,实现了动态近似解耦,静态完全解耦,具有响应速度快、超调量小的品质。在MATLAB仿真基础上,利用PCS-B型过程控制系统实验装置进行了实验研究,实验结果表明,采用神经网络PID控制器,动态调节时间和稳态误差明显减小,获得更加满意的控制效果。
按照“集中管理、分散控制”的思想,设计了以工控机为上位机、PLC为下位机,组态软件MCGS为操作平台的城市集中供热监控系统。在MCGS环境下,完成了供热系统监控中心的主画面、神经网络PID控制画面、操作面板画面、数据显示和历史曲线显示等界面的设计。该方案的应用可以提高供热企业的管理水平和经济效益。
Central heating is one of the main infrastructures of modern cities. The installation of calorimeter, achieving household measurement, has become an inevitable trend of development for our urban central heating. Using computer technology and advanced control method, monitoring and controlling heating system, implementing variable flow operation, can overcome the shortcomings of poor quality, hot and cold inhomogeneous, wasted energy, unreasonable fees and management level backward in fixed flow heating system in past. Against(consider) the characteristics, of coupling, and lagging of variable flow heating system, secondary network temperature control programs and constant pressure point pressure control program are designed, and decoupling control is realized by using neural network.
Although the static decoupling method is simple, easy to realize, the decoupling can not be made thoroughly which can make the system unsteady. According to the coupling relationship of temperature of water supply and return, a decoupling method that static decoupling combines with PID decoupling based on diagonal recurrent neural network (DRNN) is proposed. In the method, the intensity of coupling is reduced by using static decoupling and dynamic decoupling is realized by using PID decoupling based on DRNN, to overcome coupling of temperature of secondary water supply and return. Meanwhile, consider the defect of that parameter regulation is difficult and bad adaptive capacity for conventional PID controller, neural network PID controller based on DRNN is designed. DRNN network is regarded as online identification and can be adjusted the right value of the network automatically, to realize PID controller parameter adjustment online. Based on analysis of theories above, adopting the methods of static decoupling, conventional PID decoupling and neural network PID decoupling, MATLAB simulations are carried for mathematical model of heat exchanger established. MATLAB simulation results shows that using neural network PID decoupling control programs with static decoupling has performed dynamic approximate decoupled and static completely decoupled, with the quality of fast response and small overshoot. Furthermore, Using PCS-B process control system experimental device, experimental study has been done. The results show that neural network PID controller can improve quality of dynamic process and reduce static error in practical application. And the satisfactory effect can be reached.
According to "centralized management, decentralized control" thought, urban central heating system adopts three-tier structure. And the program of heating station monitoring control system is designed, IPC is upper computer and PLC is subordinate computer based on MCGS configuration software platform in this program. In MCGS environment, main screen, neural network PID control screen, operation panel screen and data curve are designed for monitoring center in heating system. The application of this program would improve remarkably management level and economy benefit for heating enterprise.
引文
[1]刘贺明.中国城市供热发展与改革情况.区域供热,2003,(3):4-8
[2]徐忠堂.发展中的中国城市集中供热.城市发展研究,2000,(4):51-58
[3]曾享麟,蔡启林,谢鲁生,等.欧洲集中供热的发展.区域供热,2002,(1):1-8
[4]公维平,张学君.影响集中供热、热电联产发展的原因与对策.节能,1998,(12):11-13
[5]叶树人,邢城.集中供热供冷系统发展的探讨.煤气与热,2001,(3):241-244
[6]蔡启林.集中供热系统节能潜力分析.区域供热,2000,(1):14-17
[7]江亿.集中供热网控制调节策略的探讨.区域供热,1997,(2):10-14
[8]李宜浩,王绍举,王相果.城市集中供热存在的问题及对策.建筑热能通风空调,2000,(2):36-38
[9]石兆玉.供热系统运行调节与控制.北京:清华大学出版社,1994
[10]王耕田,王昕琦.城市集中供热网智能协调控制系统.测控技术,2000,19(10):24-26
[11]邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通空调,2000,30(4):33-37
[12]张雁冰.丹麦哥本哈根的集中供热及其监控系统简介.区域供热,1997,(5):17-21
[13]郭幼农,张晓梅,周德键.全自动无人值守换热站在天津市供热系统中的应用.区域供热,1998,(6):54-57
[14]林敏,丁金华,田涛.计算机控制技术及工程应用.北京:国防工业出版社,2005
[15]谢剑英,贾青.微型计算机控制技术.第3版.北京:国防工业出版社,2001
[16]冯永祥,崔新民,贾颖力.分布式集中供热微机监测系统.计算机应用研究,1997,(5):35-38
[17]董志国.城市集中供热监测自控系统.工业控制计算机,2005,18(2):67-68
[18]狄洪发,江亿,秦绪忠,等.热量计量收费后供热网的运行管理.暖通空调,2000,30(5):83-86
[19]张建利,张道军,姜永成.城市供热无线遥测调度系统的研制.哈尔滨建筑大学学报,2000,33(5):85-89
[20]刘浩,戴居丰.集中供热网络微机监控系统.微计算机信息,2005,21(10-1):86-87
[21]李永汉,葛云启.集中供热管网的监控系统.煤气与热力,2003,23(8):473-475
[22]刁树杰,姜天辉,葛岷.供暖热网中的远程监测与自动控制.沈阳工业大学学报,2002,24(2):132-134
[23]GENE F.FRANKLIN,J.DAVID POWELL,ABBAS EMAMI-NAEINI.动态系统的反馈控制.朱齐丹,张丽珂,原新译.北京:电子工业出版社,2004
[24]袁亮,何宏声,杨明洁.集中供热热网的变流量调节方法的研究.新疆工学院学报,2000, 21(4):347-355
[25]石宏伟.集中供热系统的自控.煤气与热力,2001,(2):12-129
[26]邹瑜,徐伟,黄维.适合热计量的室外供热系统控制方式与分析.暖通空调,2000,30(1):57-59
[27]宣荣喜,黄兴.供热网温度跟随控制的算法与实现.西安电子科技大学学报(自然科学版),2005,32(4):622-626
[28]柏晨,安大伟,娄承芝,等.计量供热系统热源和热网的自动控制.煤气与热力,2003,23(11):669-671
[29]赖举.计量供热系统热网的自动控制.制冷与空调,2005,(4):43-49
[30]周国兵,张于峰.变流量间接供热系统的调节.暖通空调,2004,34(7):43-47
[31]王江江,安大伟,娄承芝,等.集中供热自控系统.煤气与热力,2004,24(5):260-263
[32]翁维勤,孙洪程.过程控制系统及工程.第2版.北京:化学工业出版社,1995
[33]胡寿松.自动控制原理.第4版.北京:科学出版社,2002
[34]董阿妮.变风量空调系统自适应神经元解耦控制与变静压协调控制.[西安建筑科技大学硕士论文].2004:24-47
[35]陈瑜.自整定PID控制技术在供热系统中的应用.[山东大学硕士论文].2005:7-18
[36]於仲义.变水量空调系统智能解耦控制研究.[华中科技大学硕士论文].2004:35-41
[37]王军,佟威,石磊,等.PID神经网络解耦控制技术在VAV中的应用.西北大学学报(自然科学版),2002,32(3):237-239
[38]徐丽娜.神经网络控制.哈尔滨:哈尔滨工业大学出版社,1998
[39]C.C.KU,K.Y.LEE.Dianonal Recurrent Neural Networks for Dynamic System Contral.IEEE Trans.on NN,1993,6(1):144-156
[40]P.CAMPOLUCCI,A.UNCINI.On-Line Learning Algorithms for Locally Recurrent Neural Nolworks.IEEE Trans.on Neural Nolworks,1999,10(2):253-271
[41]K.S.NARENDRA,K.PARTHASARATHY.Identification and Contral of Dynamical Systems Using Neural Nolworks.IEEE Trans.On Neural Networks,1990,1(1):4-27
[42]M.GORI,Y.BENGIO,D.VLORI R.A Learning Algorithm for Capturing the Dynamic Nature of Speech.Inter.Joint Corf,on Neural Networks,1989,(2):417-423
[43]F.R.GAO,F.L.WANG,M.Z.LI.A Simple Nonlinear Controller with Diagonal Recurrent Neural Network.Chemical Engineering Science,2000,55(7):1283-1288
[44]Q.SONG,J.Z.XIAO,S.CHAI.Robust Back Propagation Training Algoritbm For Multilayered Neural Tracking Controller.IEEE Transactions On Neural Networks,1999,10(5):1133-1141
[45]A.T.MASSOUD,H.A.ELMARAGHY,T.LAHDHIRI.On the Robust Nonlinear Motion Position and Force Control of Flexible Joints Robot Manipulators.Journal of Intellinent and Robotic Systems,1999,25(2):227-254
[46]D.J.XIAO,O.B.FAMILONI.A Diagonal Recurrent Neural Network-Based Hybrid Direct Adaptive SPSA Control System.IEEE Transactions on Automatic Control,1999,44(7):1469-1473.
[47]A.BRIAN,A.W.BRUCE.Nonlinear PID Control with Partial State Knowledge:Damping Without Derivatives.Int J Robotics Research,2000,19(8):715-731
[48]S.HOMAYOUN.A New Class of Nonlinear PID Controllers with Robotic Applications.J of Robotic Systerms,1998,15(3):161-181.
[49]R.J.WAI,W.K.LIU.Nonlinear Decoupled Control for Linear Induction Motor Servo-Drive Using the Slidingmode Technique.IEE Proc D Control Theory and Applicacions,2001,148(3):217-231
[50]X.X.J/AN,X.JING.New Fuzzy Logic Learning Control Scheme for Repetitive Trajectory Tracking Problems.Fuzzy Sets and Systems,2003,133(1):57-75
[51]A.T.P.IMTHIAS,R.P.S.NAGEBDRA.A Reinforcement Learning Approach to Automatic Generation Control.Electric Power Systems Research,2002,63(1):9-26
[52]陈平,裘丽华,王占林.基于对角回归网络的非线性系统建模.北京航空航天大学学报,2003,29(3):248-251
[53]刘红军,韩璞,于希宁.基于对角递归神经网络整定的PID解耦单元机组负荷控制系统.动力工程,2004,24(6):809-818
[54]靳其兵,曾东宁,王云华,等.多变量系统的神经网络解耦新方法.西南交通大学学报,1999,20(3):250-253
[55]付龙海,李蒙.基于PID神经网络解耦控制的变风量空调系统.东北大学学报(自然科学版),2005,40(1):13-17
[56]李辉.一种多变量模糊神经网络解耦控制器的设计.控制与决策:2006,21(5):593-596
[57]舒怀林,郭秀才,舒杰磊.注塑机料筒多段温度PID神经网络解耦控制系统.计算技术与自动化,2004,23(4):55-57
[58]杨国亮,项龙江,李钟侠.神经网络解耦控制器在单元机组负荷控制中的应用.南方冶金学院学报,2001,22(2):112-116
[59]杨青,党选举.基于DRNN的多变量解耦控制系统.自动化技术与应用,2004,23(3):14-17
[60]C.Y.LIU,X.L.SONG,Z.Y.SONG,et al.Control System Design of Heating Exchange Station Based on Fuzzy Technology.Proceedings of 2006 International Conference on Machine Learning and Cybernetics,2006,1(7):365-369
[61]Z.Y.SONG,C.Y.LIU,X.L.SONG.Application of Self-Tuning PID Control Based on Diagonal Recurrent Neural Network in Crystallization Process.Proceedings of 2006 International Conference on Machine Learning and Cybernetics,2006,1(3):380-384
[62]C.Y.LIU,K.LI,Z.Y.SONG,et al.Decoupling Control and Simulation for Variable Flow Heating Systems.International conference on Machine Learning and Cybernetice,2007,(1):419-424
[63]C.Y.LIU,Y.HIRASHIMA,A.INOUE.An Adaptive Fuzzy Controller with CMAC-Based Scaling Factors.The 15Th Triennial World Congress,2002,(1):232-237
[64]C.Y.LIU.A Fuzzy Controller with Adaptive Scaling Factors.Internation Symposium on Advanced control of Industrial Process,2002,(1):97-102
[65]C.Y.LIU,X.L.SONG.The Design and Analysis on Nonlinear Scaling Factors of Fuzzy Controller.The 4th Asian Control Conference,2002,(1):176-181
[66]C.Y.LIU,X.L.SONG.The Design and Analysis on a New Type Fuzzy Controller.IEEE,The First International Conference on Machine Learning and Cybernetice,2002,(4):2165-2167
[67]X.L.SONG.Robust PID Control for Steam Superheater.International Conference on Machine Learning and Cybernetice,2004,(1):253-257
[68]柴天佑.多变量自适应解耦控制及应用.北京:科学出版社,2001
[69]刘金琨.先进PID控制及其MATLAB仿真.北京:电子工业出版社,2003
[70]薛定宇.控制系统计算机辅助设计.北京:清华大学出版社,1996
[71]张文明,刘志军.组态软件控制技术.北京:清华大学出版社,北京交通大学出版社,2006
[72]吴中俊,黄永红.可编程序控制器原理及应用.北京:机械工业出版社,2003
[73]王立明.基于PLC和工控机的混凝土搅拌站测控系统设计.[西安建筑科技大学硕士论文].2006:34-38
[74]马国华.监控组态软件及其应用.北京:清华大学出版社,2001
[75]袁秀英.组态控制技术.北京:电子工业出版社,2003
[2]徐忠堂.发展中的中国城市集中供热.城市发展研究,2000,(4):51-58
[3]曾享麟,蔡启林,谢鲁生,等.欧洲集中供热的发展.区域供热,2002,(1):1-8
[4]公维平,张学君.影响集中供热、热电联产发展的原因与对策.节能,1998,(12):11-13
[5]叶树人,邢城.集中供热供冷系统发展的探讨.煤气与热,2001,(3):241-244
[6]蔡启林.集中供热系统节能潜力分析.区域供热,2000,(1):14-17
[7]江亿.集中供热网控制调节策略的探讨.区域供热,1997,(2):10-14
[8]李宜浩,王绍举,王相果.城市集中供热存在的问题及对策.建筑热能通风空调,2000,(2):36-38
[9]石兆玉.供热系统运行调节与控制.北京:清华大学出版社,1994
[10]王耕田,王昕琦.城市集中供热网智能协调控制系统.测控技术,2000,19(10):24-26
[11]邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通空调,2000,30(4):33-37
[12]张雁冰.丹麦哥本哈根的集中供热及其监控系统简介.区域供热,1997,(5):17-21
[13]郭幼农,张晓梅,周德键.全自动无人值守换热站在天津市供热系统中的应用.区域供热,1998,(6):54-57
[14]林敏,丁金华,田涛.计算机控制技术及工程应用.北京:国防工业出版社,2005
[15]谢剑英,贾青.微型计算机控制技术.第3版.北京:国防工业出版社,2001
[16]冯永祥,崔新民,贾颖力.分布式集中供热微机监测系统.计算机应用研究,1997,(5):35-38
[17]董志国.城市集中供热监测自控系统.工业控制计算机,2005,18(2):67-68
[18]狄洪发,江亿,秦绪忠,等.热量计量收费后供热网的运行管理.暖通空调,2000,30(5):83-86
[19]张建利,张道军,姜永成.城市供热无线遥测调度系统的研制.哈尔滨建筑大学学报,2000,33(5):85-89
[20]刘浩,戴居丰.集中供热网络微机监控系统.微计算机信息,2005,21(10-1):86-87
[21]李永汉,葛云启.集中供热管网的监控系统.煤气与热力,2003,23(8):473-475
[22]刁树杰,姜天辉,葛岷.供暖热网中的远程监测与自动控制.沈阳工业大学学报,2002,24(2):132-134
[23]GENE F.FRANKLIN,J.DAVID POWELL,ABBAS EMAMI-NAEINI.动态系统的反馈控制.朱齐丹,张丽珂,原新译.北京:电子工业出版社,2004
[24]袁亮,何宏声,杨明洁.集中供热热网的变流量调节方法的研究.新疆工学院学报,2000, 21(4):347-355
[25]石宏伟.集中供热系统的自控.煤气与热力,2001,(2):12-129
[26]邹瑜,徐伟,黄维.适合热计量的室外供热系统控制方式与分析.暖通空调,2000,30(1):57-59
[27]宣荣喜,黄兴.供热网温度跟随控制的算法与实现.西安电子科技大学学报(自然科学版),2005,32(4):622-626
[28]柏晨,安大伟,娄承芝,等.计量供热系统热源和热网的自动控制.煤气与热力,2003,23(11):669-671
[29]赖举.计量供热系统热网的自动控制.制冷与空调,2005,(4):43-49
[30]周国兵,张于峰.变流量间接供热系统的调节.暖通空调,2004,34(7):43-47
[31]王江江,安大伟,娄承芝,等.集中供热自控系统.煤气与热力,2004,24(5):260-263
[32]翁维勤,孙洪程.过程控制系统及工程.第2版.北京:化学工业出版社,1995
[33]胡寿松.自动控制原理.第4版.北京:科学出版社,2002
[34]董阿妮.变风量空调系统自适应神经元解耦控制与变静压协调控制.[西安建筑科技大学硕士论文].2004:24-47
[35]陈瑜.自整定PID控制技术在供热系统中的应用.[山东大学硕士论文].2005:7-18
[36]於仲义.变水量空调系统智能解耦控制研究.[华中科技大学硕士论文].2004:35-41
[37]王军,佟威,石磊,等.PID神经网络解耦控制技术在VAV中的应用.西北大学学报(自然科学版),2002,32(3):237-239
[38]徐丽娜.神经网络控制.哈尔滨:哈尔滨工业大学出版社,1998
[39]C.C.KU,K.Y.LEE.Dianonal Recurrent Neural Networks for Dynamic System Contral.IEEE Trans.on NN,1993,6(1):144-156
[40]P.CAMPOLUCCI,A.UNCINI.On-Line Learning Algorithms for Locally Recurrent Neural Nolworks.IEEE Trans.on Neural Nolworks,1999,10(2):253-271
[41]K.S.NARENDRA,K.PARTHASARATHY.Identification and Contral of Dynamical Systems Using Neural Nolworks.IEEE Trans.On Neural Networks,1990,1(1):4-27
[42]M.GORI,Y.BENGIO,D.VLORI R.A Learning Algorithm for Capturing the Dynamic Nature of Speech.Inter.Joint Corf,on Neural Networks,1989,(2):417-423
[43]F.R.GAO,F.L.WANG,M.Z.LI.A Simple Nonlinear Controller with Diagonal Recurrent Neural Network.Chemical Engineering Science,2000,55(7):1283-1288
[44]Q.SONG,J.Z.XIAO,S.CHAI.Robust Back Propagation Training Algoritbm For Multilayered Neural Tracking Controller.IEEE Transactions On Neural Networks,1999,10(5):1133-1141
[45]A.T.MASSOUD,H.A.ELMARAGHY,T.LAHDHIRI.On the Robust Nonlinear Motion Position and Force Control of Flexible Joints Robot Manipulators.Journal of Intellinent and Robotic Systems,1999,25(2):227-254
[46]D.J.XIAO,O.B.FAMILONI.A Diagonal Recurrent Neural Network-Based Hybrid Direct Adaptive SPSA Control System.IEEE Transactions on Automatic Control,1999,44(7):1469-1473.
[47]A.BRIAN,A.W.BRUCE.Nonlinear PID Control with Partial State Knowledge:Damping Without Derivatives.Int J Robotics Research,2000,19(8):715-731
[48]S.HOMAYOUN.A New Class of Nonlinear PID Controllers with Robotic Applications.J of Robotic Systerms,1998,15(3):161-181.
[49]R.J.WAI,W.K.LIU.Nonlinear Decoupled Control for Linear Induction Motor Servo-Drive Using the Slidingmode Technique.IEE Proc D Control Theory and Applicacions,2001,148(3):217-231
[50]X.X.J/AN,X.JING.New Fuzzy Logic Learning Control Scheme for Repetitive Trajectory Tracking Problems.Fuzzy Sets and Systems,2003,133(1):57-75
[51]A.T.P.IMTHIAS,R.P.S.NAGEBDRA.A Reinforcement Learning Approach to Automatic Generation Control.Electric Power Systems Research,2002,63(1):9-26
[52]陈平,裘丽华,王占林.基于对角回归网络的非线性系统建模.北京航空航天大学学报,2003,29(3):248-251
[53]刘红军,韩璞,于希宁.基于对角递归神经网络整定的PID解耦单元机组负荷控制系统.动力工程,2004,24(6):809-818
[54]靳其兵,曾东宁,王云华,等.多变量系统的神经网络解耦新方法.西南交通大学学报,1999,20(3):250-253
[55]付龙海,李蒙.基于PID神经网络解耦控制的变风量空调系统.东北大学学报(自然科学版),2005,40(1):13-17
[56]李辉.一种多变量模糊神经网络解耦控制器的设计.控制与决策:2006,21(5):593-596
[57]舒怀林,郭秀才,舒杰磊.注塑机料筒多段温度PID神经网络解耦控制系统.计算技术与自动化,2004,23(4):55-57
[58]杨国亮,项龙江,李钟侠.神经网络解耦控制器在单元机组负荷控制中的应用.南方冶金学院学报,2001,22(2):112-116
[59]杨青,党选举.基于DRNN的多变量解耦控制系统.自动化技术与应用,2004,23(3):14-17
[60]C.Y.LIU,X.L.SONG,Z.Y.SONG,et al.Control System Design of Heating Exchange Station Based on Fuzzy Technology.Proceedings of 2006 International Conference on Machine Learning and Cybernetics,2006,1(7):365-369
[61]Z.Y.SONG,C.Y.LIU,X.L.SONG.Application of Self-Tuning PID Control Based on Diagonal Recurrent Neural Network in Crystallization Process.Proceedings of 2006 International Conference on Machine Learning and Cybernetics,2006,1(3):380-384
[62]C.Y.LIU,K.LI,Z.Y.SONG,et al.Decoupling Control and Simulation for Variable Flow Heating Systems.International conference on Machine Learning and Cybernetice,2007,(1):419-424
[63]C.Y.LIU,Y.HIRASHIMA,A.INOUE.An Adaptive Fuzzy Controller with CMAC-Based Scaling Factors.The 15Th Triennial World Congress,2002,(1):232-237
[64]C.Y.LIU.A Fuzzy Controller with Adaptive Scaling Factors.Internation Symposium on Advanced control of Industrial Process,2002,(1):97-102
[65]C.Y.LIU,X.L.SONG.The Design and Analysis on Nonlinear Scaling Factors of Fuzzy Controller.The 4th Asian Control Conference,2002,(1):176-181
[66]C.Y.LIU,X.L.SONG.The Design and Analysis on a New Type Fuzzy Controller.IEEE,The First International Conference on Machine Learning and Cybernetice,2002,(4):2165-2167
[67]X.L.SONG.Robust PID Control for Steam Superheater.International Conference on Machine Learning and Cybernetice,2004,(1):253-257
[68]柴天佑.多变量自适应解耦控制及应用.北京:科学出版社,2001
[69]刘金琨.先进PID控制及其MATLAB仿真.北京:电子工业出版社,2003
[70]薛定宇.控制系统计算机辅助设计.北京:清华大学出版社,1996
[71]张文明,刘志军.组态软件控制技术.北京:清华大学出版社,北京交通大学出版社,2006
[72]吴中俊,黄永红.可编程序控制器原理及应用.北京:机械工业出版社,2003
[73]王立明.基于PLC和工控机的混凝土搅拌站测控系统设计.[西安建筑科技大学硕士论文].2006:34-38
[74]马国华.监控组态软件及其应用.北京:清华大学出版社,2001
[75]袁秀英.组态控制技术.北京:电子工业出版社,2003
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |