刨花板调施胶过程预测控制研究
|
摘要
刨花板生产线调施胶过程控制的水平,直接影响刨花板的物理力学性能和甲醛释放量,直接决定调胶的配比精度和施胶的实时准确性。本文结合国家“948”项目“人造板施胶智能诊断控制关键技术引进”(2006-4-109)与黑龙江省科技攻关项目“刨花板施胶、混胶智能数控系统研究”(GB06A505),在详细分析刨花板调施胶过程生产工艺和控制机理的基础上,通过对预测控制理论和技术的深入研究,提出了刨花板调施胶过程预测控制方法,主要研究内容如下:
针对调胶过程中各原料的动态称量精度问题,分析了动态称量调胶过程的控制机理,建立了调胶过程动态称量模型,在此基础上,设计了调胶迭代学习控制方法。该方法通过对调胶系统进行控制尝试,以输出轨迹与给定轨迹的偏差修正不理想的控制信号,产生新的控制信号,使得系统能够用非常简单的方式和较少的先验知识处理不正确程度较高的调胶控制系统。
针对刨花板施胶过程中存在的胶液流量实时跟踪刨花流量延迟、施胶量不准确等问题,提出一种改进的广义预测控制方法。该方法利用广义预测控制的思想,将刨花板施胶过程流量控制预测模型用受控自回归积分滑动平均模型CARIMA来描述,并通过对广义预测控制与动态矩阵控制的控制律等价性分析,来推求最优控制律的参数。仿真结果表明,该方法对刨花板施胶控制系统的时延和参数变化具有很强的鲁棒性,有效提高了施胶量的实时准确性。
针对刨花板施胶过程存在的非线性特性,对典型的非线性Hammerstein模型进行了辨识方法研究。在研究中对通常算法存在的线性参数估计的一致性无法得到保证的问题,提出一种新的思路,将模型的非线性静态模块用最小二乘支持向量机模型描述,线性动态模块用状态空间模型结构描述进行辨识,并解决了最小二乘支持向量机因将二次规划问题转换成线性方程组而失去了支持向量机解的稀疏性问题。仿真结果表明,该方法增强了Hammerstein模型的鲁棒性,提高了模型辨识精度,减少了模型辨识时间。
针对刨花板施胶过程中存在的胶液流量响应刨花流量慢、滞后性强和非线性明显的现象,提出了基于改进的Hammerstein模型的预测函数控制(PFC)方法。该方法将施胶控制输入结构化,把每一时刻加入的控制输入看作若干事先选定的基函数的线性组合,使控制量的输入规律性更加明显,并利用Hammerstein模型,把施胶非线性系统的控制问题分解为线性模块的动态优化问题和非线性模块的静态求根问题。仿真结果表明,该方法可提高施胶响应的快速性,增强刨花板施胶控制系统的鲁棒性。
针对刨花板施胶过程控制系统的抗干扰问题,进行了深入研究。设计了基于KPFC-PI的刨花板施胶控制方法,将比值器和PFC作为系统外环,用以提高胶液跟踪刨花的速度和鲁棒性,系统内环路采用PI控制器,增强系统抗干扰性,并通过调节PI参数,拟合简化整个内环路,作为外环PFC的广义处理对象,用来减少干扰引起的偏差。仿真结果表明,该方法可有效提高刨花板施胶精度、保持施胶量稳定性和具有较强抗干扰性能。
结合本文上述理论研究,对刨花板调施胶控制进行了仿真实验研究。调胶仿真结果表明,采用迭代学习方法构造的迭代学习因子q可以使提前关闭量u快速调整到理想整定值;调胶实验结果表明,采用迭代学习控制可以有效提高调胶精度,保证调胶配比精度。施胶仿真实验结果表明,本文研究的三种施胶控制方法中,KPFC-PID控制方法的抗干扰能力最强,广义预测控制方法的响应效果较好,基于Hammerstein模型的预测函数控制方法对非线性情况处理最好。
During the process of producing particleboard,control level of mixed and used glue process has a direct influence on the physical and chemical properties of particleboard and formaldehyde emission.This paper combines with National 948 project,the title of which is“Key technology of diagnosis and control for wood-based board used glue”(2006-4-109) and HeiLongJiang Province scientific and technological project,the title of which is“Research on intelligent NC system of mixed and used glue for particleboard”(GB06A505). Through studying predictive control theory and related technology,put forward the process of particleboard an advanced predictive control technology is proposed basis on deeply analyzing production technology control mechanism of mixed and used glue for particleboard,Main research contents are as follows:
For dynamic weighing accuracy problem of various raw materials in mixed glue process, dynamic weighing model is established by the means of analyzing control mechanism of this process.Then an kind of iterative learning control method for used glue is designed.Through controlling mixed glue system,the deviation between output track and given track is used to rectify the non-ideal control signal.By doing so,new control signals will be generated.With these new signals,system could be able to deal with mixed glue control system with a higher incorrect degree in a simple approach.And it only depends on less prior knowledge.
During the process of used glue,problems such as delay of tracking in real-time and inaccuracy of used glue are generated.An improved generalized predictive control method is proposed.This method based on theory of generalized predictive control.And the method describes predictive control model of the glue flow used on surface layer by controlled autoregressive moving average model CARIMA.And through analyzing equivalence of GPC and DMC control laws,parameters of optimal control law could be inquired.The simulation results show that the method could effectively improve the accuracy of controlling used glue' s volume and exhibits strong robustness to problems exited in used glue on particleboard surface layer control system,such as delay,variation of paramaters.
For the nonlinear property existed in the process of used glue,a research is carried on typical nonlinear Hammerstein model with identification method.However,consistency of linear parameters estimation can not be guaranteed by common algorithm.In these circumstances,this paper presents a new idea.The nonlinear static module of model is described by using LS-SVM model.And the linear dynamic module is identified by structure of state space model.The sparse problem caused by converting problem of quadratic planning into problem of linear equations is also solved.The simulation results show that the method enhances the robustness of Hammerstein model,improves the accuracy of the model identification,and reduces the time consumption of model identification.
In the process of used glue,there are problems,such as the response of glue flow to core layer particle flow is slow,significantly delay and obvious nonlinear.The predictive founction control(PFC)method based on improved Hammerstein model is proposed.The method will be used to structure glue control input,and to consider every control input added in every moment as a series of pre-selected linear combination of basis functions.Therefore,the input regularity of control value is more evident.And through utilizing Hammerstein model,nonlinear system' s control of used glue on core layer is divided into two parts.The one is dynamic optimization of linear system' s module.The other is statically finding roots of nonlinear module.Simulation results show that the method can improve the rapidity which is used to measure the response of used glue on core layer,and enhance robustness of used glue control system.
Research on anti-interference problem of used glue process control system for particleboard.Used glue control system is designed based on KPFC-PI.In system,ratio device and PFC work as system' s outer loop used to improve tracking speed and robustness.PI controller is worked as system's inner loop and used to improve anti-interference of system. Through adjusting PI parameters and fitting simplified inner loop,the entire inner loop worked as generalized object of the outer loop.By doing so,the deviation caused by interference will be reduced.The simulation results show that the method can effectively improve the accuracy of used glue on core layer for particleboard,maintain stability of used glue volume and possess a strong performance of anti-interference.
Combined with theoretical researches mentioned above,a simulation experiment is carried on.Mixed glue simulation results show that the use of iterative learning method for constructing iterative learning factor q can adjust advance closure volume u to ideal setting value with rapid speed;Mixed glue experimental results show that the use of iterative learning control can effectively improve the accuracy of mixed glue,and ensure the accuracy of additive ratio.Used glue simulation results show that among three types of control methods mentioned in this paper,KPFC-PID control method has the strongest anti-interference ability, generalized predictive control method responds better,and predictive functional control method based on the H model deals with nonlinear situation much better.
针对调胶过程中各原料的动态称量精度问题,分析了动态称量调胶过程的控制机理,建立了调胶过程动态称量模型,在此基础上,设计了调胶迭代学习控制方法。该方法通过对调胶系统进行控制尝试,以输出轨迹与给定轨迹的偏差修正不理想的控制信号,产生新的控制信号,使得系统能够用非常简单的方式和较少的先验知识处理不正确程度较高的调胶控制系统。
针对刨花板施胶过程中存在的胶液流量实时跟踪刨花流量延迟、施胶量不准确等问题,提出一种改进的广义预测控制方法。该方法利用广义预测控制的思想,将刨花板施胶过程流量控制预测模型用受控自回归积分滑动平均模型CARIMA来描述,并通过对广义预测控制与动态矩阵控制的控制律等价性分析,来推求最优控制律的参数。仿真结果表明,该方法对刨花板施胶控制系统的时延和参数变化具有很强的鲁棒性,有效提高了施胶量的实时准确性。
针对刨花板施胶过程存在的非线性特性,对典型的非线性Hammerstein模型进行了辨识方法研究。在研究中对通常算法存在的线性参数估计的一致性无法得到保证的问题,提出一种新的思路,将模型的非线性静态模块用最小二乘支持向量机模型描述,线性动态模块用状态空间模型结构描述进行辨识,并解决了最小二乘支持向量机因将二次规划问题转换成线性方程组而失去了支持向量机解的稀疏性问题。仿真结果表明,该方法增强了Hammerstein模型的鲁棒性,提高了模型辨识精度,减少了模型辨识时间。
针对刨花板施胶过程中存在的胶液流量响应刨花流量慢、滞后性强和非线性明显的现象,提出了基于改进的Hammerstein模型的预测函数控制(PFC)方法。该方法将施胶控制输入结构化,把每一时刻加入的控制输入看作若干事先选定的基函数的线性组合,使控制量的输入规律性更加明显,并利用Hammerstein模型,把施胶非线性系统的控制问题分解为线性模块的动态优化问题和非线性模块的静态求根问题。仿真结果表明,该方法可提高施胶响应的快速性,增强刨花板施胶控制系统的鲁棒性。
针对刨花板施胶过程控制系统的抗干扰问题,进行了深入研究。设计了基于KPFC-PI的刨花板施胶控制方法,将比值器和PFC作为系统外环,用以提高胶液跟踪刨花的速度和鲁棒性,系统内环路采用PI控制器,增强系统抗干扰性,并通过调节PI参数,拟合简化整个内环路,作为外环PFC的广义处理对象,用来减少干扰引起的偏差。仿真结果表明,该方法可有效提高刨花板施胶精度、保持施胶量稳定性和具有较强抗干扰性能。
结合本文上述理论研究,对刨花板调施胶控制进行了仿真实验研究。调胶仿真结果表明,采用迭代学习方法构造的迭代学习因子q可以使提前关闭量u快速调整到理想整定值;调胶实验结果表明,采用迭代学习控制可以有效提高调胶精度,保证调胶配比精度。施胶仿真实验结果表明,本文研究的三种施胶控制方法中,KPFC-PID控制方法的抗干扰能力最强,广义预测控制方法的响应效果较好,基于Hammerstein模型的预测函数控制方法对非线性情况处理最好。
During the process of producing particleboard,control level of mixed and used glue process has a direct influence on the physical and chemical properties of particleboard and formaldehyde emission.This paper combines with National 948 project,the title of which is“Key technology of diagnosis and control for wood-based board used glue”(2006-4-109) and HeiLongJiang Province scientific and technological project,the title of which is“Research on intelligent NC system of mixed and used glue for particleboard”(GB06A505). Through studying predictive control theory and related technology,put forward the process of particleboard an advanced predictive control technology is proposed basis on deeply analyzing production technology control mechanism of mixed and used glue for particleboard,Main research contents are as follows:
For dynamic weighing accuracy problem of various raw materials in mixed glue process, dynamic weighing model is established by the means of analyzing control mechanism of this process.Then an kind of iterative learning control method for used glue is designed.Through controlling mixed glue system,the deviation between output track and given track is used to rectify the non-ideal control signal.By doing so,new control signals will be generated.With these new signals,system could be able to deal with mixed glue control system with a higher incorrect degree in a simple approach.And it only depends on less prior knowledge.
During the process of used glue,problems such as delay of tracking in real-time and inaccuracy of used glue are generated.An improved generalized predictive control method is proposed.This method based on theory of generalized predictive control.And the method describes predictive control model of the glue flow used on surface layer by controlled autoregressive moving average model CARIMA.And through analyzing equivalence of GPC and DMC control laws,parameters of optimal control law could be inquired.The simulation results show that the method could effectively improve the accuracy of controlling used glue' s volume and exhibits strong robustness to problems exited in used glue on particleboard surface layer control system,such as delay,variation of paramaters.
For the nonlinear property existed in the process of used glue,a research is carried on typical nonlinear Hammerstein model with identification method.However,consistency of linear parameters estimation can not be guaranteed by common algorithm.In these circumstances,this paper presents a new idea.The nonlinear static module of model is described by using LS-SVM model.And the linear dynamic module is identified by structure of state space model.The sparse problem caused by converting problem of quadratic planning into problem of linear equations is also solved.The simulation results show that the method enhances the robustness of Hammerstein model,improves the accuracy of the model identification,and reduces the time consumption of model identification.
In the process of used glue,there are problems,such as the response of glue flow to core layer particle flow is slow,significantly delay and obvious nonlinear.The predictive founction control(PFC)method based on improved Hammerstein model is proposed.The method will be used to structure glue control input,and to consider every control input added in every moment as a series of pre-selected linear combination of basis functions.Therefore,the input regularity of control value is more evident.And through utilizing Hammerstein model,nonlinear system' s control of used glue on core layer is divided into two parts.The one is dynamic optimization of linear system' s module.The other is statically finding roots of nonlinear module.Simulation results show that the method can improve the rapidity which is used to measure the response of used glue on core layer,and enhance robustness of used glue control system.
Research on anti-interference problem of used glue process control system for particleboard.Used glue control system is designed based on KPFC-PI.In system,ratio device and PFC work as system' s outer loop used to improve tracking speed and robustness.PI controller is worked as system's inner loop and used to improve anti-interference of system. Through adjusting PI parameters and fitting simplified inner loop,the entire inner loop worked as generalized object of the outer loop.By doing so,the deviation caused by interference will be reduced.The simulation results show that the method can effectively improve the accuracy of used glue on core layer for particleboard,maintain stability of used glue volume and possess a strong performance of anti-interference.
Combined with theoretical researches mentioned above,a simulation experiment is carried on.Mixed glue simulation results show that the use of iterative learning method for constructing iterative learning factor q can adjust advance closure volume u to ideal setting value with rapid speed;Mixed glue experimental results show that the use of iterative learning control can effectively improve the accuracy of mixed glue,and ensure the accuracy of additive ratio.Used glue simulation results show that among three types of control methods mentioned in this paper,KPFC-PID control method has the strongest anti-interference ability, generalized predictive control method responds better,and predictive functional control method based on the H model deals with nonlinear situation much better.
引文
[1]白化奎.开发利用农业剩余物资源是解决木材短缺的重要途径.国土与自然资源研究,2008,2:93-94
[2]陆仁书.刨花板制造学.北京:中国林业出版社,1994:4
[3]中国农业百科全书总编辑委员会森林工业卷编辑委员会,中国农业百科全书编辑部.中国农业百科全书·森林工业卷.北京:农业出版社,1993:10-12
[4]戴永务,刘燕娜,余建辉.世界人造板生产和贸易状况变动分析中国人造板,2007,9:1-5
[5]胡广斌.世界其余地区生产能力增长迅猛.中国人造板,2008,6:35-39
[6]杨燕南,吕永来.聚焦我国林工一体化.中国林业产业,2008,9:30-37
[7]国家林业局编.中国林业统计年鉴.北京:中国林业出版社,2006
[8]吕斌,孔庆嫒.我国人造板质量现状分析.第四届全国人造板工业科技发展研讨会论文集,2005:31-37
[9]周定国.我国刨花板工业的创新展望.木材工业,2006,20(2):31-32
[10]回顾与展望.中国人造板,2008,3:1-5
[11]陆仁书.人造板科学与技术——陆仁书文集.哈尔滨:东北林业大学出版社,2002:292-298
[12]施家琳.采用微电子技术提高国产刨花板生产线技术水平.木材加工机械,1990,2:1-5
[13]孙玲芳,严椿绶.刨花板调供胶PLC自动控制系统的设计.林产工业,1997,24(6):32-36
[14]莫金海,邹云屏,何少佳.刨花板调供胶自动控制系统的分析与设计.木材工业,2006,20(3):10-13
[15]傅万四,王焕珂,王晓军.工业计算机调供胶系统的研制.木材加工机械,1995,1:10-13
[16]傅万四.人造板调供胶应用技术研究.木材加工机械,2003,5:21-23
[17]南生春,傅万四.PLC 在人造板调供胶控制中的应用.木材加工机械,2003,6:18-20
[18]傅万四,袁东.刨花板生产线的刨花计量与控制技术.木材加工机械,2005,1:1-5
[19]白崇彪,傅万四,曲闻远,文如泉.刨花板施胶比自动控制模式研究.木材加工机械,2007,1:33-35
[20]唐玄峰,花军,苏威.绿色人造板调施胶技术发展趋向.林业机械与木工设备,2004,(32):4-6
[21]祖海燕,陈雪梅,张怡卓.中密度纤维板调施胶技术的应用和发展趋势.木工机床, 2007,2:10-13
[22]郭亚维.浅谈刨花板调施胶技术控制现状及发展趋向.辽宁省交通高等专科学校学报,2007,9(2):84-85
[23]花军,曹军,唐铉峰等.纤维板的调施胶技术.东北林业大学学报,2005,33(1):96-98
[24]杨琼,施金国,刀刃锋.刨花板调供胶系统工艺控制探讨.林业机械与木工设备,2007,35(5):48-50
[25]袁东,余颖,郑尧.我国人造板机械发展现状及走势分析.木材加工机械,2007,1:47-50
[26]俞金寿.工业过程先进控制技术.上海:华东理工大学出版社,2008,1:106-145
[27]J.Richalet,et al.Model Predictive Heuristic Control:Applications to Industrial Processes.Automatica,1978,14(5):413-428
[28]Rouhani R,Mahra R K.Model Algorithmic Control(MAC);Basic Theoretical Properties.Automatica,1982,18(4):401-414
[29]C.R.Cutle,B.L.Ramaker.Dynamic Matrix Control-A Computer Control Algorithm.In:Proceedings of the 1980 Joint Automatic Control Conference V.1.San Francisco:American Automatic Control Council,1980:WP5 B
[30]R.M.C.De Keyser,A.R.Van Cauwenberghe.Extended Prediction Seif-adaptive Control.In:Barker H A,Young P C eds.Identification and System Parameter Estimation 1985 V.2.Oxford:Pergamon Press.1985:1255-1260
[31]D.W.Clarke,C.Mohtadi,P.S.Tuffs.Generalized Predictive Control.Automatica,1987,23(2):137-162
[32]C.E.Garcia,M.Morari.Internal Model Control:A Unifying Review and Some New Results.Ind.Engng Chem.Process Des.Dev.,1982,21(2):308-323
[33]R.Findeisen,F.Allgower.An introduction to nonlinear model predictive control.Proceeding of 21st Benelux Meeting on Systems and Control,2002:1-23
[34]席裕庚.预测控制.北京:国防工业出版社,1993,6:5-9
[35]舒迪前.预测控制系统及其应用.北京:机械工业出版社,1996
[36]E.F.Camacho,C.Bordons.Model Predictive Control.Springer,1999
[37]F.Allgower,A.Zheng,(Eds.).Nonlinear model predictive control,progress in systems and control theory.Vol.26.Basel,Boston,Berlin:Birkhauser Verlag,1999
[38]B.Kouvaritakis,M.Cannon(Eds.).Nonlinear predictive control theory and practice.London:The Institution of Electrical Engineers,2001
[39]J.M.Maciejowski.Predictive control:with constraints.Pearson Education Limited,2002
[40]徐湘元.预测控制的线性方法、非线性方法和神经网络方法.华南理工大学博士论文,2000:2
[41]魏环.连续时间广义预测控制及其实现方法研究.北京化工大学博士论文,2008
[42]Richalet J.,et al.Predictive Functional Control-application to Fast and Accurate Robots.IFAC 10th World Congress,Munich,PRG,1987:251-258
[43]张日东.非线性预测控制及应用研究.浙江大学博士论文,2007
[44]L.O.Santos,P.A.Afonso,J.A.Castro,N.M.Oliveira and L.T.Biegler.On-line implementation of nonlinear MPC:an experimental case study.Control Engineering Practice.2001,9(8):847-857
[45]M.A.Henson,D.E.Seborg.Adaptive nonlinear control of a pH neutralization process.IEEE Transaction on Control Systems Technology.1994,2(3):169-182
[46]H.Genceli,M.Nikolaou.Design of robust constrained model-predictive controllers with Volterra series.AIChE Journal,1995,41(9):2098-2107
[47]张泉灵,王树青.基于 Hammerstein模 型的非线性预测函数控制.浙江大学学报(工学版),2002,36(2):119-122
[48]L.C.Ania,E.Osvaldo,J.L.Agamennoni.A nonlinear model predictive control system based on Wiener piecewise linear models.Journal of process control,2003,13:655-666
[49]L.P.Wang.Discrete model predictive controller design using Laguerre functions.Journal of Process Control,2005,14:131-142
[50]H.H.J.Bloemen,M.Cannon and B.Kouvaritakis.An interpolation strategy for discretetime bilinear MPC problems.IEEE Transactions on Automatic Control.2002,47(5):775-778
[51]胡致强.基于动态线性逼近的非线性系统预测控制.电机与控制学报.2001,5(3):185-189
[52]J.Abonyi,L.Nagy,F.Szeifert.Fuzzy model-based predictive control by instantaneous linearization.Fuzzy Sets and Systems.2001,120:109-122
[53]W.H.Chen.Predictive control of general nonlinear systems using approximation.IEE Proceedings of Control Theory and Applications.2004,151(2):137-144
[54]J.Zhang,J.Morris.Nonlinear model predictive control based on multiple local linear models.1n Proceedings of the American Control Conference.Arlington,VA,June 25-27,2001,5:3503-3508
[55]Van den Boom,T.J.J.On feedback linearization in LMI-based nonlinear MPC.In Proceedings of the American Control Conference.Philadelphia,Pennsylvania,June 1998,3:1684-1688
[56]L.Giarre,D.Bauso,P.Falugi,et al..LPV model identification for gain scheduling control:An application to rotating stall and surge control problem.Control Engineering Practice.2006,14:351-361
[57]F.D.Palma,L.Magni.A multi-model structure for model predictive control.Annual Reviews in Control.2004,28:47-52
[58]Porfirio,C.R.,E.Almeida Neto and D.Odloak,Multi-model predictive control of an industrial C3/C4 sputter,Control Engineering Practice,July 2003,11(7):765-779
[59]M.Mahfoufa,S.Kandiahb,D.A.Linkensa.Fuzzy model-based predictive control using an ARX structure with feed forward.Fuzzy Sets and Systems,2002,125:39-59
[60]B.Liu,Z.Jiang,et al..A Fuzzy Predictive Control Algorithm Based on Discrete Optimization and Its Application to Nonlinear System//Proceedings of the Second International Conference on Machine Learning and Cybernetics,2003,Xi'an,China.USA:IEEE,2003:2506-2511
[61]Q.Li,B.C.Qu,et al..Study of Fuzzy Generalized Predictive Control Algorithm on Nonlinear Systems//Proceedings of the First International Conference on Innovative Computing,Information and Control,2006,Beijing,China.USA:IEEE Computer Society,2006:437-440
[62]Alfredo Nunez,et al..Hybrid Predictive Control Based on Fuzzy Model//2006 IEEE International Conference on Fuzzy Systems,Canada.USA:IEEE,2006,6:1947-1953
[63]D.P.Huang,A.R.Van Cauwenberghe.Neural Network-Based Multiple Feedback Long Range Predictive Control.Neurocomputing,1998,18(1):127-139
[64]P.H.Sorensen,M.Norgaard,O.Ravn.Implemen ration of Neural Network Based Non-Linear Predictive Control.Neurocomputing,1999,28(1):37-51
[65]Z.G.Fu,S.F.Wang.Nonlinear Neural Predictive Control with Control Constraints //Proceedings of the 3~(rd)World Congress on Intelligent Control and Automation(in Chinese),2000,Hefei,China.USA:IEEE,2000:1081-1083
[66]H.Jazayeri-Rad.The nonlinear model-predictive control of a chemical plant using multiple neural networks Neural Comput&Applic(S0941-0643),2004,13(1):2-15
[67]刘雪芹,刘晓华.基于多神经元模型的非线性系统预测控制.控制过程,2005,12(S):128-130
[68]W.M.Zhong,D.Y.Pi,Y.X.Sun.Study on SVM Based Model Predictive Control//Proceedings of the 5~(th)Word Congress on Intelligent Control and Automation(in Chinese),2004,Hangzhou,China.USA:IEEE,2004:607-610
[69]W.M.Zhong,D.Y.Pi,Y,X.Sun.An Approach of Nonlinear Model Multi-step-ahead Predictive Control Based on SVM.Lecture Notes in Computer Science(S0302-9743),2005,3516(1):1036-1039
[70]W.M.Zhong,G.L He,D.Y.Pi,et al..SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control.Chinese J.Chem.Eng.(S1004-9541),2005,13(3):373-379
[71]M.G.Na,B.R.Upadhyaya.Model Predictive-Control of an SP-100 Space Reactor Using Support Vector Regression and Genetic Optimization.IEEE TRANSACTIONS ON NUCLEAR SCIENCE(S0018-9499),2006,53(4):2318-2327
[72]X.C.Xi,A.N.Poo,S.K.Chou.Support vector regression model predictive control on a HVAC plant[J].Control Engineering Practice(S0967-0661),2007,15(8):897-908
[73]X.J.Liu,.J.Z.Liu.Neuro-Fuzzy Generalized Predictive Control of Boiler Steam Temperature//Proceedings of the 6~(th)World Congress on Intelligent Control and Automation,2006,Dalian China.USA:IEEE,2006:6531-6535
[74]C.H.Lu,C.C.Tsai.Generalized predictive control using recurrent fuzzy neural networks for industrial processes.Journal of Process Control(S0959-1524),2007,17(1):83-92
[75]Y.P.Huang,S.F.Wang.Identifying the fuzzy grey prediction model by genetic algorithms//Proceedings of IEEE International Conference on Evolutionary Computation,1996.USA:IEEE,1996:720-725
[76]D.X.Huang,Y.H.Jin.The application of wavelet neural networks to nonlinear predictive control//international Conference on Neural Networks,1997,Texas,USA.Piscataway,NJ,USA,1997:724-727
[77]朱学峰.CSTR 的非线性预测控制.华南理工大学学报(自然科学版).1995,23(6):7-16
[78]H.Peng,T.Ozaki,Y.Toyoda,et al..RBF-ARX model-based nonlinear system modeling predictive control with application to a NOx decomposition process.Control Engineering Practice.2004,12:191-203
[79]罗国娟.阶梯式预测控制器的参数整定研究.中国科学技术大学博士论文,2006
[80]S.J.Qin,T.A.Badgwell.A survey of industrial model predictive control technology.Control Engineering Practice,2003,11:733-764
[81]H.J.Rho,Y.J.Huh and H.Ku.Rhee.Application of adaptive model-predictive control to a batch MMA polymerization reactor.Chemical Engineering Science,1998,53(21):3729-3739
[82]H.Bouhenchir,M.Cabassud and M.V.Le Lann.Predictive functional control for the temperature control of a chemical batch reactor,Computers & Chemical Engineering,In Press,Corrected Proof,Available online 17 April 2006
[83]D.L.Yu and J.B.Gomm.Implementation of neural network predictive control to a multivariable chemical reactor.Control Engineering Practice,2003,11(11):1315-1323
[84]G.Mourue,D.Dochain,V.Wertz and P.Descamps.Identification and control of am industrial polymerisation reactor.Control Engineering Practice,2004,12(7),909-915
[85]U.Volk,D.W.Kniese,R.Hahn,R.Haber and U.Schmitz.Optimized multivariable predictive control of an industrial distillation column considering haxd and soft constraints.Control Engineering Practice,2005,13(7):913-927
[86]薛美盛,孙德敏,吴刚.火电厂锅炉主蒸汽压力的阶梯式广义预测控制.中国科学 技术大学学报,2002,32(6):685-689
[87]A.Sanchez-Lopez,G.Arroyo-Figueroa and A.Villavicencio-Ramirez.Advanced control algorithms for steam temperature regulation of thermal power plants.International Journal of Electrical Power & Energy Systems,2004,26(10):779-785
[88]D.Shi,N.H.E1-Farra,et al..Predictive control of particle size distribution in particulate processes.Chemical Engineering Science,2006,61(1):268-281
[89]Y.Wang and J.Tan.Dual-target predictive control and application in food extrusion.Control Engineering Practice,2000,8(9):1055-1062
[90]H.Didriksen.Model based predictive control of a rotary dryer.Chemical Engineering Journal,2002,86(1-2):53-60
[91]A.H.Gonzalez,D.Odloak and J.L.Marchetti.Predictive control applied to heatexchanger networks,Chemical Engineering and Processing,In Press,Available online 23March 2006
[92]中国标准出版社第一编辑室.木材工业标准汇编(人造板)(第二版).北京:中国标准出版社,2005,8:3
[93]刘恩永.人造板生产工艺.北京:高等教育出版社,2002,6:28-163
[94]庞庆海.人造板机械设备.哈尔滨:东北林业大学出版社,1997
[95]吴建国.刨花板调胶系统.木材加工机械,1987,4:10-13
[96]魏福玉,徐知行,唐可洪.刨花板调供胶自动控制系统.吉林工业大学学报,1997,27(4):93-96
[97]李东光.脲醛树脂胶粘剂.北京:化学工业出版社,2002,7:259,(2):413
[98]《木材工业实用大全》编辑委员会《木材工业实用大全·刨花板卷》编写组.木材工业实用大全:刨花板卷.北京:中国林业出版社,1998:121-140
[99]黄祖泰.中纤板生产线计算机辅助系统的研究.计算机应用研究,1996,5:55-56
[100]于红.基于可编程控制器的调胶控制系统设计.自动化技术与应用,2008,27(1):104-105
[101]孙明轩,黄宝健.迭代学习控制.北京:国防工业出版社,1999
[102]S.Arimoto,S.Kawamura and F.Miyazaki.Bettering operation of robots by learning.J.of Robotic Systems,1984,1(2):123-140
[103]KL.Moore,M.Dahleh and SP.Bhattacharyya.Iterative learning control:a survey and new results.Journal of Robotic Systems,1992,9(5):563-594
[104]KL.Moore.Iterative learning control-an expository overview.Applied and Computational Controls,Signal Processing and Circuits,1998,1:151-214
[105]H.S.Lee,Z.Bien.Study of robustness of iterative learning control with non-zero initial error.Int.J.Control,1996,64(3):345-359
[106]M.French,G.Munde,E.Rogers and D.H.Owens.Recent developments in adaptive iterative learning control.In:Proceedings of the 38th IEEE Conference on Decision and Control.Phoenix,1999:264-269
[107]D.H.Owens,E.Rogers.Iterative learning control-recent progress and open research problems,IEE Seminar Learning Systems for Control,2000,7/1-7/3
[108]方忠,韩正之,陈彭年.迭代学习控制新进展.控制理论与应用,2002,19(2):161-166
[109]X.D.Li,T.W.S.Chow.Iterative learning control for linear time-variant discrete systems based on 2-D system theory.IEE Proc.-Control Theory Appl.,2005,152(1):13-18
[110]李书臣,李平,徐心和,胡玉娥.迭代学习控制理论现状与展望.系统仿真学报,2005,17(4):904-908
[111]K.L.Moore.Iterative learning control for deterministic systems.London:Springer,1993
[112]林辉,王林.迭代学习控制理论.西安:西北工业大学出版社,1998
[113]Z.Bien,J.X.Xu.Iterative Learning Control:Analysis,Design,Integration and Applications.Kluwer Academic Publishers,1998
[114]Y.Q.Chen,C.Y.Wen.Iterative Learning Control:Convergence,Robustness and Applications.Springer-Verleg London,1999
[115]J.X.Xu,Y.Tan.Linear and Nonlinear Iterative Learning Control.Springer-Verlag Berlin Heidelberg,2003
[116]谢胜利,田森平,谢振东.迭代学习控制的理论与应用.北京:科学出版社,2005
[117]于少娟,齐向东,吴聚华.迭代学习控制理论及应用.北京:机械工业出版社,2005
[118]季立伟.迭代自学习动态称量配料方法及其在混凝土搅拌系统中的应用.浙江大学硕士学位论文,2001
[119]杜寿兵,吴付岗.迭代学习控制在配料控制系统中的应用.计算机测量与控制,2006,14(4):479-481
[120]王伟.广义预测控制理论及其应用.北京:科学出版社,2001
[121]李少远.全局工况系统预测控制及其应用.北京:科学出版社,2008:17-19
[122]E.F.Camacho.Constrained generalized predictive control.IEEE Trans.Automat.Contr.,1993,38(2):327
[123]B.R.Maner,F.J.Doyle,B.A.Ogunnaike,et al..Nonlinear model predictive control of a simulated multivariable polymerization reactor using secondorder Volterra models.Automatica,1996,32(9):1285-1301
[124]李国勇,谢克明.隐式广义预测自校正控制算法的混合仿真研究.系统仿真学报,1999,11(3):157-158
[125]X.H.Zhang,Z.Q.Chen,Z..Z.Yuan.Global convergence of adaptive predictive controller based on least squares algorithm.Journal of Systems Engineering and Electronics,2003,14(4):70-73
[126]李国勇.输入受限的隐式广义预测控制算法的仿真研究.系统仿真学报.2004,16(7):1533-1535
[127]朱学莉,齐维贵,伏润,赵梅.GPC隐式算法及其在供热控制中的仿真研究.系统仿真学报.2005,17(9):2224-2217
[128]K.S.Narendra,P.G.Gallman.An iterative method for the identification of nonlinear systems using a Hammerstein model.IEEE Trans on Automatic Control,1966,11(3):546-550
[129]S.Billings.Idetification of nonlinear systems-A survey.Proc.of IEEE,1980,Part D(127):272-285
[130]E.Bai.An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems.Automatica,1998,34(3):333-338
[131]J.C.Gomez,E.Baeyens.Identification of multivariable Hammerstein systems using rational orthonormal bases.Proc.of the 39th IEEE Conf.on Decision and Control.Sydney,Australia:Institute of Electrical and Electronics Engineers Inc.,2000,1:2849-2854
[132]I.Goethals,K.Pelckmans,J.K.A.Suykens,et al..Subspace identification of hammerstein systems using least squares support vector machines.IEEE Trans on Automatic Control:Special Issue on System Identification,2005,50(10):1509-1519
[133]V.Vapnik.The Nature of Statistical Learning Theory.New York:Springer,1999
[134]金伟良,袁雪霞.基于 LS-SVM 的结构可靠度响应面分析方法.浙江大学学报(工学版),2007,41(1):44-47
[135]J.A.K.Suykens,J.Vandewalle.Least squares support vector machine classifiers.Neural Processing Letters,1999,9(3):293-300
[136]甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机.浙江大学学报(工学版),2007,41(2):245-248
[137]吴亚锋,姜节胜.结构模态参数的子空间辨识方法.应用力学学报,2001,18(SI):319-321
[138]P.Van Overschee,B.De Moor.N4SID:Subspace algorithms for the identification of combined deterministicstochastic systems.Automatica,Special Issue on Statistical Signal Processing and Control,1994,30(1):75-93
[139]马艳,曾庆福,李志舜.子空间辨识方法的改进.推进技术.2001.22(4):319-321
[140]H.A.F.Tulleken.Generelized binary noise test-signal concept for improved identification-experiement design.Automatica,1990,26(1):37-499
[141]Y.C.Zhu,P.P.J.van den Bosch.Optimal closed-loop identification test design for internal model control.Automatica,2000,36:1237-1241
[142]H.B.Kuntze,A.Jacubasch,J.Richalet,et al..“On the predictive functional control of elastic industrial robot,” in Proc.Gr.CDC.Conf.,Athens,1986,(25):1877-1881
[143]J.Richalet.Industrial application of model based predictive control.Automatica,1993,29(5):1251-1270
[144]王树青,金晓明.先进控制技术应用实例.北京:化学工业出版社,2005
[2]陆仁书.刨花板制造学.北京:中国林业出版社,1994:4
[3]中国农业百科全书总编辑委员会森林工业卷编辑委员会,中国农业百科全书编辑部.中国农业百科全书·森林工业卷.北京:农业出版社,1993:10-12
[4]戴永务,刘燕娜,余建辉.世界人造板生产和贸易状况变动分析中国人造板,2007,9:1-5
[5]胡广斌.世界其余地区生产能力增长迅猛.中国人造板,2008,6:35-39
[6]杨燕南,吕永来.聚焦我国林工一体化.中国林业产业,2008,9:30-37
[7]国家林业局编.中国林业统计年鉴.北京:中国林业出版社,2006
[8]吕斌,孔庆嫒.我国人造板质量现状分析.第四届全国人造板工业科技发展研讨会论文集,2005:31-37
[9]周定国.我国刨花板工业的创新展望.木材工业,2006,20(2):31-32
[10]回顾与展望.中国人造板,2008,3:1-5
[11]陆仁书.人造板科学与技术——陆仁书文集.哈尔滨:东北林业大学出版社,2002:292-298
[12]施家琳.采用微电子技术提高国产刨花板生产线技术水平.木材加工机械,1990,2:1-5
[13]孙玲芳,严椿绶.刨花板调供胶PLC自动控制系统的设计.林产工业,1997,24(6):32-36
[14]莫金海,邹云屏,何少佳.刨花板调供胶自动控制系统的分析与设计.木材工业,2006,20(3):10-13
[15]傅万四,王焕珂,王晓军.工业计算机调供胶系统的研制.木材加工机械,1995,1:10-13
[16]傅万四.人造板调供胶应用技术研究.木材加工机械,2003,5:21-23
[17]南生春,傅万四.PLC 在人造板调供胶控制中的应用.木材加工机械,2003,6:18-20
[18]傅万四,袁东.刨花板生产线的刨花计量与控制技术.木材加工机械,2005,1:1-5
[19]白崇彪,傅万四,曲闻远,文如泉.刨花板施胶比自动控制模式研究.木材加工机械,2007,1:33-35
[20]唐玄峰,花军,苏威.绿色人造板调施胶技术发展趋向.林业机械与木工设备,2004,(32):4-6
[21]祖海燕,陈雪梅,张怡卓.中密度纤维板调施胶技术的应用和发展趋势.木工机床, 2007,2:10-13
[22]郭亚维.浅谈刨花板调施胶技术控制现状及发展趋向.辽宁省交通高等专科学校学报,2007,9(2):84-85
[23]花军,曹军,唐铉峰等.纤维板的调施胶技术.东北林业大学学报,2005,33(1):96-98
[24]杨琼,施金国,刀刃锋.刨花板调供胶系统工艺控制探讨.林业机械与木工设备,2007,35(5):48-50
[25]袁东,余颖,郑尧.我国人造板机械发展现状及走势分析.木材加工机械,2007,1:47-50
[26]俞金寿.工业过程先进控制技术.上海:华东理工大学出版社,2008,1:106-145
[27]J.Richalet,et al.Model Predictive Heuristic Control:Applications to Industrial Processes.Automatica,1978,14(5):413-428
[28]Rouhani R,Mahra R K.Model Algorithmic Control(MAC);Basic Theoretical Properties.Automatica,1982,18(4):401-414
[29]C.R.Cutle,B.L.Ramaker.Dynamic Matrix Control-A Computer Control Algorithm.In:Proceedings of the 1980 Joint Automatic Control Conference V.1.San Francisco:American Automatic Control Council,1980:WP5 B
[30]R.M.C.De Keyser,A.R.Van Cauwenberghe.Extended Prediction Seif-adaptive Control.In:Barker H A,Young P C eds.Identification and System Parameter Estimation 1985 V.2.Oxford:Pergamon Press.1985:1255-1260
[31]D.W.Clarke,C.Mohtadi,P.S.Tuffs.Generalized Predictive Control.Automatica,1987,23(2):137-162
[32]C.E.Garcia,M.Morari.Internal Model Control:A Unifying Review and Some New Results.Ind.Engng Chem.Process Des.Dev.,1982,21(2):308-323
[33]R.Findeisen,F.Allgower.An introduction to nonlinear model predictive control.Proceeding of 21st Benelux Meeting on Systems and Control,2002:1-23
[34]席裕庚.预测控制.北京:国防工业出版社,1993,6:5-9
[35]舒迪前.预测控制系统及其应用.北京:机械工业出版社,1996
[36]E.F.Camacho,C.Bordons.Model Predictive Control.Springer,1999
[37]F.Allgower,A.Zheng,(Eds.).Nonlinear model predictive control,progress in systems and control theory.Vol.26.Basel,Boston,Berlin:Birkhauser Verlag,1999
[38]B.Kouvaritakis,M.Cannon(Eds.).Nonlinear predictive control theory and practice.London:The Institution of Electrical Engineers,2001
[39]J.M.Maciejowski.Predictive control:with constraints.Pearson Education Limited,2002
[40]徐湘元.预测控制的线性方法、非线性方法和神经网络方法.华南理工大学博士论文,2000:2
[41]魏环.连续时间广义预测控制及其实现方法研究.北京化工大学博士论文,2008
[42]Richalet J.,et al.Predictive Functional Control-application to Fast and Accurate Robots.IFAC 10th World Congress,Munich,PRG,1987:251-258
[43]张日东.非线性预测控制及应用研究.浙江大学博士论文,2007
[44]L.O.Santos,P.A.Afonso,J.A.Castro,N.M.Oliveira and L.T.Biegler.On-line implementation of nonlinear MPC:an experimental case study.Control Engineering Practice.2001,9(8):847-857
[45]M.A.Henson,D.E.Seborg.Adaptive nonlinear control of a pH neutralization process.IEEE Transaction on Control Systems Technology.1994,2(3):169-182
[46]H.Genceli,M.Nikolaou.Design of robust constrained model-predictive controllers with Volterra series.AIChE Journal,1995,41(9):2098-2107
[47]张泉灵,王树青.基于 Hammerstein模 型的非线性预测函数控制.浙江大学学报(工学版),2002,36(2):119-122
[48]L.C.Ania,E.Osvaldo,J.L.Agamennoni.A nonlinear model predictive control system based on Wiener piecewise linear models.Journal of process control,2003,13:655-666
[49]L.P.Wang.Discrete model predictive controller design using Laguerre functions.Journal of Process Control,2005,14:131-142
[50]H.H.J.Bloemen,M.Cannon and B.Kouvaritakis.An interpolation strategy for discretetime bilinear MPC problems.IEEE Transactions on Automatic Control.2002,47(5):775-778
[51]胡致强.基于动态线性逼近的非线性系统预测控制.电机与控制学报.2001,5(3):185-189
[52]J.Abonyi,L.Nagy,F.Szeifert.Fuzzy model-based predictive control by instantaneous linearization.Fuzzy Sets and Systems.2001,120:109-122
[53]W.H.Chen.Predictive control of general nonlinear systems using approximation.IEE Proceedings of Control Theory and Applications.2004,151(2):137-144
[54]J.Zhang,J.Morris.Nonlinear model predictive control based on multiple local linear models.1n Proceedings of the American Control Conference.Arlington,VA,June 25-27,2001,5:3503-3508
[55]Van den Boom,T.J.J.On feedback linearization in LMI-based nonlinear MPC.In Proceedings of the American Control Conference.Philadelphia,Pennsylvania,June 1998,3:1684-1688
[56]L.Giarre,D.Bauso,P.Falugi,et al..LPV model identification for gain scheduling control:An application to rotating stall and surge control problem.Control Engineering Practice.2006,14:351-361
[57]F.D.Palma,L.Magni.A multi-model structure for model predictive control.Annual Reviews in Control.2004,28:47-52
[58]Porfirio,C.R.,E.Almeida Neto and D.Odloak,Multi-model predictive control of an industrial C3/C4 sputter,Control Engineering Practice,July 2003,11(7):765-779
[59]M.Mahfoufa,S.Kandiahb,D.A.Linkensa.Fuzzy model-based predictive control using an ARX structure with feed forward.Fuzzy Sets and Systems,2002,125:39-59
[60]B.Liu,Z.Jiang,et al..A Fuzzy Predictive Control Algorithm Based on Discrete Optimization and Its Application to Nonlinear System//Proceedings of the Second International Conference on Machine Learning and Cybernetics,2003,Xi'an,China.USA:IEEE,2003:2506-2511
[61]Q.Li,B.C.Qu,et al..Study of Fuzzy Generalized Predictive Control Algorithm on Nonlinear Systems//Proceedings of the First International Conference on Innovative Computing,Information and Control,2006,Beijing,China.USA:IEEE Computer Society,2006:437-440
[62]Alfredo Nunez,et al..Hybrid Predictive Control Based on Fuzzy Model//2006 IEEE International Conference on Fuzzy Systems,Canada.USA:IEEE,2006,6:1947-1953
[63]D.P.Huang,A.R.Van Cauwenberghe.Neural Network-Based Multiple Feedback Long Range Predictive Control.Neurocomputing,1998,18(1):127-139
[64]P.H.Sorensen,M.Norgaard,O.Ravn.Implemen ration of Neural Network Based Non-Linear Predictive Control.Neurocomputing,1999,28(1):37-51
[65]Z.G.Fu,S.F.Wang.Nonlinear Neural Predictive Control with Control Constraints //Proceedings of the 3~(rd)World Congress on Intelligent Control and Automation(in Chinese),2000,Hefei,China.USA:IEEE,2000:1081-1083
[66]H.Jazayeri-Rad.The nonlinear model-predictive control of a chemical plant using multiple neural networks Neural Comput&Applic(S0941-0643),2004,13(1):2-15
[67]刘雪芹,刘晓华.基于多神经元模型的非线性系统预测控制.控制过程,2005,12(S):128-130
[68]W.M.Zhong,D.Y.Pi,Y.X.Sun.Study on SVM Based Model Predictive Control//Proceedings of the 5~(th)Word Congress on Intelligent Control and Automation(in Chinese),2004,Hangzhou,China.USA:IEEE,2004:607-610
[69]W.M.Zhong,D.Y.Pi,Y,X.Sun.An Approach of Nonlinear Model Multi-step-ahead Predictive Control Based on SVM.Lecture Notes in Computer Science(S0302-9743),2005,3516(1):1036-1039
[70]W.M.Zhong,G.L He,D.Y.Pi,et al..SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control.Chinese J.Chem.Eng.(S1004-9541),2005,13(3):373-379
[71]M.G.Na,B.R.Upadhyaya.Model Predictive-Control of an SP-100 Space Reactor Using Support Vector Regression and Genetic Optimization.IEEE TRANSACTIONS ON NUCLEAR SCIENCE(S0018-9499),2006,53(4):2318-2327
[72]X.C.Xi,A.N.Poo,S.K.Chou.Support vector regression model predictive control on a HVAC plant[J].Control Engineering Practice(S0967-0661),2007,15(8):897-908
[73]X.J.Liu,.J.Z.Liu.Neuro-Fuzzy Generalized Predictive Control of Boiler Steam Temperature//Proceedings of the 6~(th)World Congress on Intelligent Control and Automation,2006,Dalian China.USA:IEEE,2006:6531-6535
[74]C.H.Lu,C.C.Tsai.Generalized predictive control using recurrent fuzzy neural networks for industrial processes.Journal of Process Control(S0959-1524),2007,17(1):83-92
[75]Y.P.Huang,S.F.Wang.Identifying the fuzzy grey prediction model by genetic algorithms//Proceedings of IEEE International Conference on Evolutionary Computation,1996.USA:IEEE,1996:720-725
[76]D.X.Huang,Y.H.Jin.The application of wavelet neural networks to nonlinear predictive control//international Conference on Neural Networks,1997,Texas,USA.Piscataway,NJ,USA,1997:724-727
[77]朱学峰.CSTR 的非线性预测控制.华南理工大学学报(自然科学版).1995,23(6):7-16
[78]H.Peng,T.Ozaki,Y.Toyoda,et al..RBF-ARX model-based nonlinear system modeling predictive control with application to a NOx decomposition process.Control Engineering Practice.2004,12:191-203
[79]罗国娟.阶梯式预测控制器的参数整定研究.中国科学技术大学博士论文,2006
[80]S.J.Qin,T.A.Badgwell.A survey of industrial model predictive control technology.Control Engineering Practice,2003,11:733-764
[81]H.J.Rho,Y.J.Huh and H.Ku.Rhee.Application of adaptive model-predictive control to a batch MMA polymerization reactor.Chemical Engineering Science,1998,53(21):3729-3739
[82]H.Bouhenchir,M.Cabassud and M.V.Le Lann.Predictive functional control for the temperature control of a chemical batch reactor,Computers & Chemical Engineering,In Press,Corrected Proof,Available online 17 April 2006
[83]D.L.Yu and J.B.Gomm.Implementation of neural network predictive control to a multivariable chemical reactor.Control Engineering Practice,2003,11(11):1315-1323
[84]G.Mourue,D.Dochain,V.Wertz and P.Descamps.Identification and control of am industrial polymerisation reactor.Control Engineering Practice,2004,12(7),909-915
[85]U.Volk,D.W.Kniese,R.Hahn,R.Haber and U.Schmitz.Optimized multivariable predictive control of an industrial distillation column considering haxd and soft constraints.Control Engineering Practice,2005,13(7):913-927
[86]薛美盛,孙德敏,吴刚.火电厂锅炉主蒸汽压力的阶梯式广义预测控制.中国科学 技术大学学报,2002,32(6):685-689
[87]A.Sanchez-Lopez,G.Arroyo-Figueroa and A.Villavicencio-Ramirez.Advanced control algorithms for steam temperature regulation of thermal power plants.International Journal of Electrical Power & Energy Systems,2004,26(10):779-785
[88]D.Shi,N.H.E1-Farra,et al..Predictive control of particle size distribution in particulate processes.Chemical Engineering Science,2006,61(1):268-281
[89]Y.Wang and J.Tan.Dual-target predictive control and application in food extrusion.Control Engineering Practice,2000,8(9):1055-1062
[90]H.Didriksen.Model based predictive control of a rotary dryer.Chemical Engineering Journal,2002,86(1-2):53-60
[91]A.H.Gonzalez,D.Odloak and J.L.Marchetti.Predictive control applied to heatexchanger networks,Chemical Engineering and Processing,In Press,Available online 23March 2006
[92]中国标准出版社第一编辑室.木材工业标准汇编(人造板)(第二版).北京:中国标准出版社,2005,8:3
[93]刘恩永.人造板生产工艺.北京:高等教育出版社,2002,6:28-163
[94]庞庆海.人造板机械设备.哈尔滨:东北林业大学出版社,1997
[95]吴建国.刨花板调胶系统.木材加工机械,1987,4:10-13
[96]魏福玉,徐知行,唐可洪.刨花板调供胶自动控制系统.吉林工业大学学报,1997,27(4):93-96
[97]李东光.脲醛树脂胶粘剂.北京:化学工业出版社,2002,7:259,(2):413
[98]《木材工业实用大全》编辑委员会《木材工业实用大全·刨花板卷》编写组.木材工业实用大全:刨花板卷.北京:中国林业出版社,1998:121-140
[99]黄祖泰.中纤板生产线计算机辅助系统的研究.计算机应用研究,1996,5:55-56
[100]于红.基于可编程控制器的调胶控制系统设计.自动化技术与应用,2008,27(1):104-105
[101]孙明轩,黄宝健.迭代学习控制.北京:国防工业出版社,1999
[102]S.Arimoto,S.Kawamura and F.Miyazaki.Bettering operation of robots by learning.J.of Robotic Systems,1984,1(2):123-140
[103]KL.Moore,M.Dahleh and SP.Bhattacharyya.Iterative learning control:a survey and new results.Journal of Robotic Systems,1992,9(5):563-594
[104]KL.Moore.Iterative learning control-an expository overview.Applied and Computational Controls,Signal Processing and Circuits,1998,1:151-214
[105]H.S.Lee,Z.Bien.Study of robustness of iterative learning control with non-zero initial error.Int.J.Control,1996,64(3):345-359
[106]M.French,G.Munde,E.Rogers and D.H.Owens.Recent developments in adaptive iterative learning control.In:Proceedings of the 38th IEEE Conference on Decision and Control.Phoenix,1999:264-269
[107]D.H.Owens,E.Rogers.Iterative learning control-recent progress and open research problems,IEE Seminar Learning Systems for Control,2000,7/1-7/3
[108]方忠,韩正之,陈彭年.迭代学习控制新进展.控制理论与应用,2002,19(2):161-166
[109]X.D.Li,T.W.S.Chow.Iterative learning control for linear time-variant discrete systems based on 2-D system theory.IEE Proc.-Control Theory Appl.,2005,152(1):13-18
[110]李书臣,李平,徐心和,胡玉娥.迭代学习控制理论现状与展望.系统仿真学报,2005,17(4):904-908
[111]K.L.Moore.Iterative learning control for deterministic systems.London:Springer,1993
[112]林辉,王林.迭代学习控制理论.西安:西北工业大学出版社,1998
[113]Z.Bien,J.X.Xu.Iterative Learning Control:Analysis,Design,Integration and Applications.Kluwer Academic Publishers,1998
[114]Y.Q.Chen,C.Y.Wen.Iterative Learning Control:Convergence,Robustness and Applications.Springer-Verleg London,1999
[115]J.X.Xu,Y.Tan.Linear and Nonlinear Iterative Learning Control.Springer-Verlag Berlin Heidelberg,2003
[116]谢胜利,田森平,谢振东.迭代学习控制的理论与应用.北京:科学出版社,2005
[117]于少娟,齐向东,吴聚华.迭代学习控制理论及应用.北京:机械工业出版社,2005
[118]季立伟.迭代自学习动态称量配料方法及其在混凝土搅拌系统中的应用.浙江大学硕士学位论文,2001
[119]杜寿兵,吴付岗.迭代学习控制在配料控制系统中的应用.计算机测量与控制,2006,14(4):479-481
[120]王伟.广义预测控制理论及其应用.北京:科学出版社,2001
[121]李少远.全局工况系统预测控制及其应用.北京:科学出版社,2008:17-19
[122]E.F.Camacho.Constrained generalized predictive control.IEEE Trans.Automat.Contr.,1993,38(2):327
[123]B.R.Maner,F.J.Doyle,B.A.Ogunnaike,et al..Nonlinear model predictive control of a simulated multivariable polymerization reactor using secondorder Volterra models.Automatica,1996,32(9):1285-1301
[124]李国勇,谢克明.隐式广义预测自校正控制算法的混合仿真研究.系统仿真学报,1999,11(3):157-158
[125]X.H.Zhang,Z.Q.Chen,Z..Z.Yuan.Global convergence of adaptive predictive controller based on least squares algorithm.Journal of Systems Engineering and Electronics,2003,14(4):70-73
[126]李国勇.输入受限的隐式广义预测控制算法的仿真研究.系统仿真学报.2004,16(7):1533-1535
[127]朱学莉,齐维贵,伏润,赵梅.GPC隐式算法及其在供热控制中的仿真研究.系统仿真学报.2005,17(9):2224-2217
[128]K.S.Narendra,P.G.Gallman.An iterative method for the identification of nonlinear systems using a Hammerstein model.IEEE Trans on Automatic Control,1966,11(3):546-550
[129]S.Billings.Idetification of nonlinear systems-A survey.Proc.of IEEE,1980,Part D(127):272-285
[130]E.Bai.An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems.Automatica,1998,34(3):333-338
[131]J.C.Gomez,E.Baeyens.Identification of multivariable Hammerstein systems using rational orthonormal bases.Proc.of the 39th IEEE Conf.on Decision and Control.Sydney,Australia:Institute of Electrical and Electronics Engineers Inc.,2000,1:2849-2854
[132]I.Goethals,K.Pelckmans,J.K.A.Suykens,et al..Subspace identification of hammerstein systems using least squares support vector machines.IEEE Trans on Automatic Control:Special Issue on System Identification,2005,50(10):1509-1519
[133]V.Vapnik.The Nature of Statistical Learning Theory.New York:Springer,1999
[134]金伟良,袁雪霞.基于 LS-SVM 的结构可靠度响应面分析方法.浙江大学学报(工学版),2007,41(1):44-47
[135]J.A.K.Suykens,J.Vandewalle.Least squares support vector machine classifiers.Neural Processing Letters,1999,9(3):293-300
[136]甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机.浙江大学学报(工学版),2007,41(2):245-248
[137]吴亚锋,姜节胜.结构模态参数的子空间辨识方法.应用力学学报,2001,18(SI):319-321
[138]P.Van Overschee,B.De Moor.N4SID:Subspace algorithms for the identification of combined deterministicstochastic systems.Automatica,Special Issue on Statistical Signal Processing and Control,1994,30(1):75-93
[139]马艳,曾庆福,李志舜.子空间辨识方法的改进.推进技术.2001.22(4):319-321
[140]H.A.F.Tulleken.Generelized binary noise test-signal concept for improved identification-experiement design.Automatica,1990,26(1):37-499
[141]Y.C.Zhu,P.P.J.van den Bosch.Optimal closed-loop identification test design for internal model control.Automatica,2000,36:1237-1241
[142]H.B.Kuntze,A.Jacubasch,J.Richalet,et al..“On the predictive functional control of elastic industrial robot,” in Proc.Gr.CDC.Conf.,Athens,1986,(25):1877-1881
[143]J.Richalet.Industrial application of model based predictive control.Automatica,1993,29(5):1251-1270
[144]王树青,金晓明.先进控制技术应用实例.北京:化学工业出版社,2005