内模控制方法在复杂系统中的研究与应用
|
摘要
随着现代工业的飞速发展,生产过程变得越来越复杂,常常包含着非自衡对象,变结构对象,而过程对象中又常常存在不确定性、时滞性、多变量耦合、输入输出受约束等复杂特征。对于这些复杂的被控工业过程,采用常规的内模控制方法难以达到理想的控制效果。为了更好的发挥内模控制的优势,针对复杂系统中的各种特殊被控对象研究出简单、有效、实用的新型内模控制方法已成为当前研究领域及现代工业的迫切需求之一。本文正是基于这个立足点,以内模控制为基本控制方法,从内模控制结构的改进、内模结构的稳定性与鲁棒性分析、滤波器的改进等方向对复杂系统中一些难题提出了一些解决方案和改进措施,主要研究内容和已取得的研究成果如下：
(1)阐述了系统辨识的原理和几种常见的辨识方法。重点研究了粒子群算法(PSO)和无约束多维寻优法(Powell),通过分析这两种算法的优缺点,提出了一种基于Powell的PSO辨识算法,通过对化工过程中某对象模型进行辨识,仿真实验证明了所提出算法的有效性和鲁棒性。研究了工业现场中常见的几种响应不规范情况,该方法能有效地解决在初值非稳态、终值非稳态、输出发散等响应不规范情形下的模型辨识问题。
(2)介绍了内模控制的结构、原理和设计方法等,研究了内模控制和经典反馈控制的关系,以及滤波器的设计方法。研究了时滞环节的各种近似处理方法,对一阶Taylor近似、一阶Pade近似、二阶对称Pade近似、二阶非对称Pade近似以及全极点近似法进行了比较。基于以上五种近似方法针对一阶时滞系统和二阶时滞系统设计了内模控制器,并推导了IMC-PID控制器的参数整定公式。
(3)研究了基于内模控制方法的多变量时滞系统的解耦控制问题,采用了两种内模控制方案：第一种是基于V规范型的多变量解耦内模控制,即让多变量内模控制器具有V规范型结构,使其同时具有解耦补偿器和内模控制器的作用,推导了控制器的设计方法,得到了控制器设计的一般通式；第二种方案是为了解决多变量时滞系统的传递函数矩阵难以进行因式分解的问题,通过求取过程传递函数矩阵的逆和最优对角分解矩阵,来实现闭环系统解耦以及控制器响应时滞最小的内模控制方法。最后,通过仿真和效果对比说明了这两种方法的有效性。
(4)分析了传统内模控制方法在非自衡对象中应用的不足,提出了两种改进的内模控制结构。第一种方法,是在传统的内模控制结构中添加了一个比例控制器和一个比例微分控制器,分别用于镇定不稳定对象和控制系统的抗干扰特性及鲁棒特性,并采用全极点近似法对被控对象的纯滞后项进行近似处理；第二种方法,是为了克服外部干扰的影响,把反馈信号加在控制变量上,即在传统的一自由度内模控制结构的基础上添加了一个扰动估计器用于抑制干扰信号,实现了系统跟踪性能和抗扰性能的解耦控制,并有较好的动态特性。最后通过仿真实验结果证明了这两种方法的有效性。
(5)针对化工过程中常见的非方系统,提出了两种非方系统的内模控制方法。第一种方法,是采用非方有效相对增益法对非方系统进行稳定性分析,选出输入输出关联性最强的最优子系统,再对最优子系统进行回路分析和配对,根据分析结果分别设计了两种内模控制器和一个强鲁棒性分散PID控制器；第二种方法,是针对含有时滞和非最小相位零点的非方耦合系统,提出了一种基于奇异值分解的解耦内模控制方法,该方法通过添加补偿项来实现对非方系统的解耦并消除不可实现因素。并根据奇异值分解法设计了一种非对角矩阵滤波器,使控制系统具有较好的高维解耦能力和强鲁棒性。最后通过仿真结果证明了这两种方法的有效性和可行性。
通过对上述各种工业对象的研究,进一步发挥了内模控制方法的优势,在理论上使内模控制更为完善,在实际应用中也使其应用面更为广泛。
With the rapid development of modern industry, the production process become much more complex than before, which always contains integrator and time delay process, variable structure object. And these controlled object often has the characteristics of uncertainty, time delay, multivariable coupling, input and output are constrained ect.. By using traditional internal model control method to solve these complex problems, the desired control effect can be hardly achieved. In order to fully play the advantages of internal model control, to develop some effective, simple and practical internal model control methods for a variety of special controlled objects has become one of the urgent needs for research area and modern industrial area. Based on this standpoint, taking internal model control as the basic control method, some solutions and improvements are proposed for the problems of complex systems. These solutions and improvements include the improvement of internal model control structure, the stability and robustness analysis of internal model structure, and the improvement of the filter. In this dissertation, the main study contents and the research results are as follows:
(1) Represented the principles of system identification and several common identification methods. Mainly researched particle swarm optimization (PSO) and unconstrained multidimensional optimization algorithm(Powell), by analyzing the strengths and weaknesses of PSO algorithm and Powell algorithm, PSO-Powell identification algorithm was proposed. Through the simulation of chemical plant model illustrated the effectiveness and robustness of the proposed identification method. The unstandardized response situation of the industrial field had been researched, and this proposed method can carry out model identification in the condition of unsteady state initial value, unsteady final value, as well as Divergence output, ect.
(2) The principle, structure and design method of internal model control are systematically represented. The relationship between IMC and classical feedback control, and the design method of filter are researched. A variety of time delay approximation methods had been researched. Based on these approximation methods internal model controllers were designed for the first order system with time delay and the second order system with time delay, derived the IMC-PID tuning formulas.
(3) The decoupling and control problem of multivariable processes with time delays has been investigated. Two novel multivariable internal model control methods are adopted. The first method isⅤnorm multivariable decoupling internal model control. The multivariable internal model controller has V norm structure, which has the functions of decoupling compensator and internal model controller. The controller design procedures were deduced, and the general formula of controller design was obtained. The second method is Max-Max internal model control method for multivariable system with multiple time delays. By calculating the inverse of transfer function matrix and the optimal diagonal matrix, the closed-loop system can be decoupled and the control response with minimum time delay. Finally the simulation results illustrated the effectiveness of these two methods.
(4) In view of the limitation of the traditional internal model control method in the integrator and time delay process. Two improvement IMC structure had been proposed. The first method is that a proportional controller and a proportional differential controller were added to the traditional internal model control structure, to stabilize unstable object and control the disturbance rejection performance and robustness of the system respectively. Meanwhile, All-pole approximation method was adopted to approximate the delay item of the system. The second method is that to overcome the impact of external interference, the feedback signal is added to the control variables, that is equal to added a perturbation estimator to traditional IMC structure, the decoupling control of the system was realized, and the system has good dynamic characteristics. Finally, the simulation results illustrated the effectiveness of these two methods.
(5) The internal model control method for non-square system has been investigated in the dissertation. Two internal model control methods had been proposed. The first method is that to analysis the stability of non-square system by non-square effective relative gain, and chosen the optimal subsystem which has the most relevant inputs and outputs. After analyzing this optimal subsystem, two internal model controllers and one strong robustness decentralized PID controller were designed based on the analysis result. The second method is that for the non-square system with time-delay and non-minimum phase zero, based on analytical analysis and generalized inverse a decoupling internal model control was proposed. This method can realize decoupling of non-square processes and eradicating unrealizable factors by inserting compensated terms. Meanwhile, a non diagonal filter is designed on the basis of SVD matrix theory, which makes control system could bear the capacity of high-dimensional decoupling and fast response, and has strong robustness. Finally the simulation results illustrated the effectiveness of these two methods.
Though the researches of the above industrial objects, the advantage of internal model control is further developed, complete the internal model control in theory, and made the application of internal model control much wider in practical application.
(1)阐述了系统辨识的原理和几种常见的辨识方法。重点研究了粒子群算法(PSO)和无约束多维寻优法(Powell),通过分析这两种算法的优缺点,提出了一种基于Powell的PSO辨识算法,通过对化工过程中某对象模型进行辨识,仿真实验证明了所提出算法的有效性和鲁棒性。研究了工业现场中常见的几种响应不规范情况,该方法能有效地解决在初值非稳态、终值非稳态、输出发散等响应不规范情形下的模型辨识问题。
(2)介绍了内模控制的结构、原理和设计方法等,研究了内模控制和经典反馈控制的关系,以及滤波器的设计方法。研究了时滞环节的各种近似处理方法,对一阶Taylor近似、一阶Pade近似、二阶对称Pade近似、二阶非对称Pade近似以及全极点近似法进行了比较。基于以上五种近似方法针对一阶时滞系统和二阶时滞系统设计了内模控制器,并推导了IMC-PID控制器的参数整定公式。
(3)研究了基于内模控制方法的多变量时滞系统的解耦控制问题,采用了两种内模控制方案：第一种是基于V规范型的多变量解耦内模控制,即让多变量内模控制器具有V规范型结构,使其同时具有解耦补偿器和内模控制器的作用,推导了控制器的设计方法,得到了控制器设计的一般通式；第二种方案是为了解决多变量时滞系统的传递函数矩阵难以进行因式分解的问题,通过求取过程传递函数矩阵的逆和最优对角分解矩阵,来实现闭环系统解耦以及控制器响应时滞最小的内模控制方法。最后,通过仿真和效果对比说明了这两种方法的有效性。
(4)分析了传统内模控制方法在非自衡对象中应用的不足,提出了两种改进的内模控制结构。第一种方法,是在传统的内模控制结构中添加了一个比例控制器和一个比例微分控制器,分别用于镇定不稳定对象和控制系统的抗干扰特性及鲁棒特性,并采用全极点近似法对被控对象的纯滞后项进行近似处理；第二种方法,是为了克服外部干扰的影响,把反馈信号加在控制变量上,即在传统的一自由度内模控制结构的基础上添加了一个扰动估计器用于抑制干扰信号,实现了系统跟踪性能和抗扰性能的解耦控制,并有较好的动态特性。最后通过仿真实验结果证明了这两种方法的有效性。
(5)针对化工过程中常见的非方系统,提出了两种非方系统的内模控制方法。第一种方法,是采用非方有效相对增益法对非方系统进行稳定性分析,选出输入输出关联性最强的最优子系统,再对最优子系统进行回路分析和配对,根据分析结果分别设计了两种内模控制器和一个强鲁棒性分散PID控制器；第二种方法,是针对含有时滞和非最小相位零点的非方耦合系统,提出了一种基于奇异值分解的解耦内模控制方法,该方法通过添加补偿项来实现对非方系统的解耦并消除不可实现因素。并根据奇异值分解法设计了一种非对角矩阵滤波器,使控制系统具有较好的高维解耦能力和强鲁棒性。最后通过仿真结果证明了这两种方法的有效性和可行性。
通过对上述各种工业对象的研究,进一步发挥了内模控制方法的优势,在理论上使内模控制更为完善,在实际应用中也使其应用面更为广泛。
With the rapid development of modern industry, the production process become much more complex than before, which always contains integrator and time delay process, variable structure object. And these controlled object often has the characteristics of uncertainty, time delay, multivariable coupling, input and output are constrained ect.. By using traditional internal model control method to solve these complex problems, the desired control effect can be hardly achieved. In order to fully play the advantages of internal model control, to develop some effective, simple and practical internal model control methods for a variety of special controlled objects has become one of the urgent needs for research area and modern industrial area. Based on this standpoint, taking internal model control as the basic control method, some solutions and improvements are proposed for the problems of complex systems. These solutions and improvements include the improvement of internal model control structure, the stability and robustness analysis of internal model structure, and the improvement of the filter. In this dissertation, the main study contents and the research results are as follows:
(1) Represented the principles of system identification and several common identification methods. Mainly researched particle swarm optimization (PSO) and unconstrained multidimensional optimization algorithm(Powell), by analyzing the strengths and weaknesses of PSO algorithm and Powell algorithm, PSO-Powell identification algorithm was proposed. Through the simulation of chemical plant model illustrated the effectiveness and robustness of the proposed identification method. The unstandardized response situation of the industrial field had been researched, and this proposed method can carry out model identification in the condition of unsteady state initial value, unsteady final value, as well as Divergence output, ect.
(2) The principle, structure and design method of internal model control are systematically represented. The relationship between IMC and classical feedback control, and the design method of filter are researched. A variety of time delay approximation methods had been researched. Based on these approximation methods internal model controllers were designed for the first order system with time delay and the second order system with time delay, derived the IMC-PID tuning formulas.
(3) The decoupling and control problem of multivariable processes with time delays has been investigated. Two novel multivariable internal model control methods are adopted. The first method isⅤnorm multivariable decoupling internal model control. The multivariable internal model controller has V norm structure, which has the functions of decoupling compensator and internal model controller. The controller design procedures were deduced, and the general formula of controller design was obtained. The second method is Max-Max internal model control method for multivariable system with multiple time delays. By calculating the inverse of transfer function matrix and the optimal diagonal matrix, the closed-loop system can be decoupled and the control response with minimum time delay. Finally the simulation results illustrated the effectiveness of these two methods.
(4) In view of the limitation of the traditional internal model control method in the integrator and time delay process. Two improvement IMC structure had been proposed. The first method is that a proportional controller and a proportional differential controller were added to the traditional internal model control structure, to stabilize unstable object and control the disturbance rejection performance and robustness of the system respectively. Meanwhile, All-pole approximation method was adopted to approximate the delay item of the system. The second method is that to overcome the impact of external interference, the feedback signal is added to the control variables, that is equal to added a perturbation estimator to traditional IMC structure, the decoupling control of the system was realized, and the system has good dynamic characteristics. Finally, the simulation results illustrated the effectiveness of these two methods.
(5) The internal model control method for non-square system has been investigated in the dissertation. Two internal model control methods had been proposed. The first method is that to analysis the stability of non-square system by non-square effective relative gain, and chosen the optimal subsystem which has the most relevant inputs and outputs. After analyzing this optimal subsystem, two internal model controllers and one strong robustness decentralized PID controller were designed based on the analysis result. The second method is that for the non-square system with time-delay and non-minimum phase zero, based on analytical analysis and generalized inverse a decoupling internal model control was proposed. This method can realize decoupling of non-square processes and eradicating unrealizable factors by inserting compensated terms. Meanwhile, a non diagonal filter is designed on the basis of SVD matrix theory, which makes control system could bear the capacity of high-dimensional decoupling and fast response, and has strong robustness. Finally the simulation results illustrated the effectiveness of these two methods.
Though the researches of the above industrial objects, the advantage of internal model control is further developed, complete the internal model control in theory, and made the application of internal model control much wider in practical application.
引文
[1]吴登峰.单变量辨识方法的研究与工程化应用[D].北京：北京化工大学,2010.
[2]刘子宜.系统辨识方法的研究与工程化应用[D].北京：北京化工大学,2009.
[3]蒋文春.过程辨识方法的研究及实现[D].北京：北京化工大学,2009.
[4]A. SESHAGIRI, R.A.M., CHIDAMBARAM. Decoupled Smith predictor for multivariable nonsquare systems with multiple time delays[J]. Indian Institute of Science,2006,86: 235-256.
[5]潘峰,韩如成.时变大时滞系统的控制方法综述[J].仪器仪表学报,2002,23(3)：789-91.
[6]刘明鑫.时滞系统控制方法的研究[D].北京：北京化工大学,2009.
[7]陈娟.复杂系统内模控制策略的研究与实现[D].北京：北京化工大学,2006.
[8]Layton J.M.. Multivariable control theory [M]. stevenage, England: Peter Peregrines for the Institution of Electrical Engineers,1976.
[9]Maciejowski J.M.. Multivariable feedback design[M]. Workingham, England: Addison-Wesley,1989.
[10]宰文妓.一类非自衡对象的改进两自由度内模控制[D].四川：四川大学,2006.
[11]Garcia C E, M.M.. Internal model control.1.A unifying review and some new Results[J]. Industrial Engineering Chemistry Process Design and Development,1982.21(2):308-323.
[12]Richalet J A. Model predictive heuristic control:applications to an industrial Process[J]. Automatica,1978,14(5):413-428.
[13]Cutler C. R., R.L.B.. Dynamic Matrix Control-a Computer Control Algorithm.[J]. In: JACC P roceedings,1980:W P5-B.
[14]赵曜.内模控制发展综述[]].信息与控制,2000,29(6)：526-531.
[15]冯春蕾.内模控制技术及其在多变量时滞系统中的应用[D].北京：北京化工大学,2009.
[16]黄静,基于内模控制的PID控制系统的研究与应用[D].北京：北京化工大学,2006.
[17]Garcia C E, M. M.. Internal Model Control.2. Design procedure for multivariable System[J]. I&EC Process Des.Dev,1985,24(2):472-484.
[18]Garcia C E, M.M. Internal model control.3.Multivariable control law computation and tuning guidelines[J]. Industrial Engineering Chemistry Process Design and Development, 1985,24(2):484-494.
[19]Morari M and Z. E., Robust Process control[M]. Prentice-Hall,1988.
[20]薛兴昌.工业自动化系统的技术发展[J].自动化博览,2003(8)：48-50.
[21]Garcia C E, M.M.. Internal Model Control-Extention to Nonlinear System[J]. Ind. Eng. Chem. Proc. Des. Dev.,1986,25(2):403-411.
[22]Economou C G, M.M.. Internal model control:6 multiloop design[J]. Ind. Eng. Chem. Proc. Des.Dev.,1986,25:411-419.
[23]袁琴,多变量系统内模控制方法研究[D].北京：北京化工大学,2008.
[24]郑恩让,张玲.内模控制在造纸生产定量控制中的应用[J].化工自动化及仪表,2001,28(3)：23-24.
[25]王东风,王剑东,韩璞.一种多变量系统的内幕解耦控制设计方法[J].控制工程,2003,10(5)：463-465.
[26]程志强,戴连奎,孙优贤.加热炉热效率软测量与内模控制[J].信息与控制,2004,33(1)：85-88.
[27]杨锡运,徐大平,张彬.再热汽温系统的内模解藕控制[J].动力工程,2004,24(24)：529-532.
[28]Wang Qingguo, Z.Y., Chiu Minsen.. Decoupling internal model control for multivariable systems with multiple time delays[J]. Chemical Engineering Science,2002,57:115-124.
[29]Edgar C R, P.B.E.. MIMO fuzzy internal model control[J]. Automatica,2000,34:867-877.
[30]Zheng A., Kothare M V., and M. M.. Anti-windup design for internal model control[J]. Int J Control,1994,60(5):1015-1024.
[31]Sairam V, M.S., Input constraint handling and windup compensation in nonlinear control[J]. Proc of American Control Conference,1997:1734-1738.
[32]Malan S, M.E.A., Regruto D.. Robust control from data via uncertainty model sets identification[C]. Proc of the 40th IEEE Conf on Decision and Control Orlando: IEEE Press, 2001,3:2686-2691.
[33]Campi M., L.W.S., Anaerson B. D. O.. Filters for internal model control design for unstable plants[C]. Proc of 32 nd IEEE Conf on Decision and Control. San Antonio: IEEE Press, 1993:1343-1348.
[34]Rivals I., P.L.. Nonlinear internal model control using neural networks: application to processes with delay and design issues[J]. IEEE Trans on Neural Networks,2000,11(1): 80-90.
[35]王建国.时滞系统增益自适应内模PID控制方法[J].吉林大学学报(信息科学版),2004,22(4)：355-358.
[36]华建兴,席裕庚.带预估控制量二次约束的无静差FIR型内模控制器[J].控制理论与应用,1999,16(4)：492-495.
[37]潘立登,潘仰东.系统辨识与建模[M].北京：化学工业出版社,2004.
[38]莫建林,王伟,许晓鸣,张卫东.系统辨识中的闭环问题[J].控制理论与应用,2002,19(1)：9-14.
[39]李少远,蔡文剑.工业过程辨识与控制[M].北京：化学工业出版社,2005：206-214.
[40]温之建.多变量系统辨识的研究与应用[D].北京：北京化工大学,2003.
[41]夏天长.系统辨识-最小二乘法[M].熊光楞,李芳芸译.北京：清华大学出版社,1983.
[42]王永,李旺,梁青.多变量频域极大似然辨识算法研究[J].东南大学学报(自然科学版),2005,35(增刊Ⅱ)：15-21.
[43]李旭,高升,熊忠辉.一类多变量系统特殊矩形波响应在线辨识[J].动力工程,2004,24(1)：95-97.
[44]Luss R, J.T.H.I.. Optimization by Direct search and Systematic Reduction of the Size of Search Region[J]. AIChE,1973,19(4):760-765.
[45]焦嵩鸣.人工神经网络在系统辨识中的研究与应用[M].保定：华北电力大学,2002.
[46]齐建玲.一种改进的BP网络系统辨识算法的研究[J].华北航天工业学院学报,2006,16(2)：1-5.
[47]舒华,舒怀林.基于PID神经网络的多变量非线性动态系统辨识[J].计算机工程与应用,2006,12：47-49.
[48]Olufemi A. Adebiyi, A.B.C.. Dynamic neural networks partial least squares (DNNPLS) identification of multivariable processes[J]. Computers and Chemical Engineering,2003, 27:143-155.
[49]刘树安,唐非.基于遗传算法的系统辨识方法研究[J].系统工程理论与实践,2007,3：134-139.
[50]任子武,伞冶.自适应遗传算法的改进及在系统辨识中应用研究[J].系统仿真学报,2006,18(1)：41-43.
[51]徐洪泽,褚东升,张福恩.一种改进的遗传算法及其在系统辨识中的应用[J].哈尔滨工业大学学报,1997,29(4)：72-75.
[52]王月鹃,万百五.大时延过程的控制方法[J].控制理论与应用,1986,3(3)：1-12.
[53]欧林林,顾诞英,张卫东.基于幅值裕度和相位裕度的PID参数最优整定方法[J].控制理论与应用,2007,24(5)：837-840.
[54]张静,曹玉强,王强.大时滞过程的自整定PID控制器[J].测控技术,2006,25(3)：63-66.
[55]曹刚,俞海斌,徐巍华等.大时滞不稳定对象的PID控制[J].仪器仪表学报,2005,26(3)：301-303,324.
[56]严兴华,王钦若,曾永荣IMC-PID在大惯性大滞后系统中的应用及仿真[J].广东有色金属学报,2006,13(3)：217-220.
[57]褚丽丽,李春茂,郭汉桥,刘军华.时滞系统的模糊自适应PID控制研究[J].自动化技术与应用,2008,1：34-36.
[58]胡品慧,徐志前,闫峰等.滞后过程控制方法研究[V].第二十二届中国控制会议论文集,2003：277-279.
[59]王树青,金晓明,荣冈.工业生产过程的先进控制[J].化工自动化及仪表,1999,26(2)：61-65.
[60]Dahlin E.B.. Design and tuning digital controller[J]. Instr.& Contr.Sys,1968,41(7):77-83.
[61]刘金琨.先进PID控制MATLAB仿真(第2版)[M].电子工业出版社,2004.
[62]朱学峰.大林算法与Smith预估器[J].自动化与仪器仪表,1999,20(4)：122-124.
[63]胡国龙,孙优贤.预测控制进展及其应用研究[J].电力系统及其自动化学报,2003,15(1)：109-114.
[64]刘明鑫.时滞系统控制方法的研究[D].北京：北京化工大学,2009.
[65]陈娟,陈岳峰,潘立登,曹柳林.多变量时滞系统的解耦模糊内模控制[J].电机与控制学报,2006,10(2)：203-207.
[66]靳其兵,宋洪法,路香菊.大滞后系统数字内模控制器的设计及仿真研究[J].计算机仿真,2005,22(8)：151-153.
[67]孙优贤,褚键.工业过程控制技术[M].化学工业出版社,2006.
[68]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.
[69]Wu ZhiQiao, M.M.. PID Type Fuzzy and Parameters Adaptive Method[J]. Fuzzy Sets and Systems,1996,78:23-25.
[70]Kevin M Passino, S.Y.. Fuzzy Control[M]. Tsinghua University Press,2001.
[71]姚继兵,吴玲,胡学妹.一种基于遗传神经网络的模糊控制系统[J].四川大学学报,2001,33(3)：117-119.
[72]K. S. Narendra, K.P.. Identification and control of dynamical systems using neural networks[J]. IEEE Transactions on Neural Networks,1990, (1):4-24.
[73]GuillermoJ.Silva, Aniruddha Datta. S.P.B.. New results on the synthesis of PID controller[J]. Control Theory and Applieations,2002,151(1):89-94.
[74]张卫东,孙优贤,许晓鸣.一类非自衡对象的PID控制[J].自动化学报,1999,25(4)： 518-523.
[75]张井刚,刘志远,裴润.一类非自衡对象的二自由度PID控制[J].控制与决策,2002,176：886-889.
[76]Yongli Song, M.O.T., Tonghua Zhang. Stabilization and algorithm of integrator plusdead-time process using PID controlled[J]. Journal of Process Control,2009,19: 1529-1537.
[77]M. Chidambaram, R.P.S.. A simple method of tuning PID controllers for integrator/dead-time processes[J]. Computers and Chemical Engineering 2003,27:211-215.
[78]Jeffrey E. Arbogast, D.J.C.. Extension of IMC tuning correlations for non-self regulating (integrating) processes. ISA Transactions,2007,46:303-311.
[79]Chien I., P.F.. Consider IMC tuning to improve controller performance[J]. Chemistry Engineering Program,1990,10:33-41.
[80]K.J. Astrom, C.C. Hang, and B.C. Lim. A new Smith predictor for controlling a process with an integrator and long dead time. IEEE Trans. Autom. Control,1994,39(2):343-345.
[81]Xiang Lu, Y.S., Qing Guowang, Wei Xingzheng. A double two-degree-of-freedom control scheme for improved control of unstable delay processes[J]. Journal of Process Control, 2005,15:605-614.
[82]Zhang Q.C., N.R.J.E., Control of integral proeesses with dead time-1:Disturbance observer-based 2DOF control seheme[J]. IEE Proe Control Theory,2002,149(4):285-290.
[83]Tien-Li Chia, I.L.. Internal model-based control for integrating processes[J]. ISA Transactions,2010:In press.
[84]Xiu zhang Jin, J.Z.S., Ji Zheng Zhang.. Analysis and design of adaptive internal model control for unstable proeess with deadtime[C]. Proceedings of the Third International Conference on Machine Learning and Cyberneties,2004,8:983-987.
[85]宰文妓,赵曜,汪华章.一类非自衡对象的两自由度内模控制[J].四川大学学报,2006,43(3)：561-564.
[86]Gareia C.E., M.M.. State-space interpretation of model predietive control[J]. Automatica, 1994,30(4):707-717.
[87]邹涛,刘红波,李少远.锅炉汽包水位非自衡系统的预测控制[J].控制理论与应用,2004,21(3)：386-390.
[88]回立川,陈蓓,郑恩让.系统的模糊-Smith预测控制[J].微计算机信息,2005,9(1)：38-40.
[89]K. L. N. Sarma, C.M.. Centralized PI/PID controllers for nonsquare systems with RHP zero[J]. J of Indian Institute Science,2005,85(4):201-214.
[90]郭妍.非非方系统内模控制方法的研究[D].北京：北京化工大学,2010.
[91]Davison E.. Some properties of minimum phase systems and" Squared-down" systems[J]. IEEE Transactions on Automatic Control,1983,2:221-222.
[92]Treiber S.. Multivariable control of non-square systems[J]. Industrial & Engineering Chemistry Process Design and Development,1984(4):854-857.
[93]D.E. Reeves, A.Y., Interaction measures for nonsquare decentralized control structures[J]. AICHE,1989(35):603-613
[94]Jin Wen Chang, C.Y., The relative gain for non-square multivariable systems[J]. Chem. Eng. Sci.,1990,45(5):1309-1323.
[95]Isidori, A.. Nonlinear Control Systems: an Introduction Springer Verlag. New York. NY, 1989.
[96]McLain R. B., K.M.J., Henson M. A.,et al. Habituating control for non-square nonlinear processes[J]. Ind. Eng. Chem. Res,1996,35(11):4067-4077.
[97]赵大力.工业过程的先进控制策略研究[D].北京：北京化工大学,2008.
[98]Morari M., GW., Oglesby M., Prosser I.. Design of resilient processing plants. Ⅶ: Design of energy management system for unstable reactors-new insights[J]. Chemical Engineering Science,1985(2):187-198.
[99]Chiu E.J.L.a.M.S.. Robust decentralized control of nonsquare systems[J]. Chem. Engng Commun,1997,22:157-180.
[100]Wang Q., Z.B., Zhang Y.. Decoupling Smith predictor design for multivariable systems with multiple time delays[J]. Chemical Engineering Research and Design,2000, (4):565-572.
[101]Seshagiri A, C.M.. Smith delay compensator for multivariable non-square system with multiple time delays[J]. Computer and Chemical Engineering,2006,.30:1243-1255.
[102]萧明波,钱积新.预测控制中静态目标的实现[J].控制理论与应用,1997,14(3)：313-317.
[103]陈莉.非方广义系统带最坏干扰抑制的奇异LQ问题[J].高校应用数学学报,2007.22(1)：9-16.
[104]D.R. VIinson, C.G..A new measure of process output controllability[J]. Proceedings of the 1998 IFAC International Symposium on Dynamics and Control of Process Systems (DYCOPS),1998,2:700-709.
[105]D. R. Vinson, C.G.. A new measure of process output controllability[J]. Journal of Process Control,2000,10(2):185-194.
[106]P. Suganda, P.R. Krishnaswamy, and G.P. Rangaiah. On-Line Process Identification from Closed-Loop Tests Under PI Control[J]. Chemical Engineering Research and Design,1998, 76:451-457.
[107]潘立登.最优化在在线调节器整定中的应用[J].北京化工学院学报,1984(2)：34-36.
[108]U. Baur, R.I.. On-line identification of a heat exchanger with a process computer-A case study[J]. Automatica,1977, (13):487-496.
[109]Jyh Horng Chou, J.-H.S.. Jyh-Nan Shieh, On-line identification and optimal control of continuous-time systems[J]. Mathematics and Computers in Simulation,2003,63:493-503.
[110]Karim Salahshoor, M.H.. A novel online affine model identification of multivariable processes using adaptive neuro-fuzzy networks[J]. Chemical Engineering Research and Design,2010,8:155-169.
[111]张磊.时滞系统的辨识与控制[D].北京：北京化工大学,2007.
[112]Gauss C.F.. Theoria Motus Corporum Coelestium in sectionibus conicis solem ambientium[M]. Hamburg, F. Perthes and Besser,1809.
[113]L.Miskovic, A.K., D. Bonvin, M. Gevers. Closed-loop identification of multivariable systems: With or without excitation of all references?[J] Automatica,2008,44:2048-2056.
[114]Holland J.H.. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence[M]. University of Michigan Press,1992.
[115]Timothy I.Salsbury. Continuous-time model identification for closed loop control performance assessment[J]. Control Engineering Practice,2007,15:109-121.
[116]Kennedy J. and E.R. C.. Particle swarm optimization[C]. Proceedings of IEEE international Conference on Neural Networks.Piscataway,IEEE Press,1995,4:1942-1948.
[117]Eberhart R. C and K. J.. A new optimizer using particle swarm theory[C]. Proceedings of the Sixth International Sympisium on Micro Machine and Human Science. IEEE Press, Nagoya, Japan.,1995:39-43.
[118]Kennedy J., E.R.C., Shi,Y.. Swarm intelligence.[M] San Francisco: Morgan Kaufmann Publishers,2001.
[119]Kennedy J. and E.R. C, A discrete binary version of the particle swarm algorithm[C]. Proceeding of the IEEE Conference on Systems, Man and Cybernetics SMC, IEEE Press, 1997,5:4104-4108.
[120]朱志斌,张可村.不等式约束优化一个新的SQP算法[J].计算数学,2004.
[121]马俊英,罗元浩,潘立登.用改进的NLJ方法辨识闭环系统的模型参数及滤波器设计[J].北京化工大学学报,2003,30(4)：95-97.
[122]T.C.Hsia系统辨识与应用[M].长沙：中南工业大学出版社,1986.
[123]孙晓天.单变量过程辨识方法及内模控制的研究与应用[D].北京：北京化工大学,2010.
[124]Zadeh L.A., From Circuit Theory to System Theory[D]. Colorado State University,1962.
[125]L.Ljung. Asymptotic variance expressions for identified black-box transfer function models [J]. IEEE Trans. Auto. Control,1985, AC-30:834-844.
[126]L.Lunng. System Identification-Theory for the user, Prentice Hall[J]. Englewood Cliffs, NJ, 1999.
[127]潘立登.先进控制与在线优化技术及其应用[M].机械工业出版社,2009.
[128]潘立登.LJ最优化方法的改进[J].化工自动化及仪表,1985,12：10-15.
[129]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3)：416-419.
[130]Sun J., X.W.B.. A global search strategy of quantum-behaved particle swarm optimization[C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems:IEEE Press,2004:111-116.
[131]M.J.D.Powell. An Efficient Method for Finding the Minimum of a Function of Several Variables without Calculating;Derivatives[J]. The Computer Journal 1964,7(2):155-162
[132]宗伟新,慕福顺,李晋惠.数字化自动控制系统性能指标的测量方法[J].国外电子测量技术,2006,10：40-41.
[133]王树青,先进控制技术及应用[M].化学工业出版社,2001.
[134]Francis B.A., W.W.M.. The internal model principle for linear multivariable regulators[J]. AppIMath and Opt,1975,2:170-194.
[135]宋云霞,朱学峰.大时滞过程控制方法及应用[J].化工自动化及仪表,2001,28(4)：9-15.
[136]赵曜.基于内模结构的鲁棒有限拍控制极其预测控制的比较[C].1995中国控制会议论文集,武汉：中国科学技术出版社,1995：157-162.
[137]刘晨晖.多变量过程控制系统解耦理论[M].北京：水利电力出版社,1984.
[138]Y. S. Apte. Linear Multivariable control theory [M]. London: McGraw Hill Book Company, 1981.
[139]Liu C H, General Decoupling theory of multivariable process control system[M].1983, Berlin: Springer-Verlag.
[140]周彦,时变大滞后过程仿人智能模糊控制[D].广西：广西大学,2006.
[141]蒋慰孙,叶银忠,多变量控制系统分析与设计[M].北京：中国石化出版社,1997：74-92.
[142]JohnGerry. How to control process with large dead time[J]. Control Engineering Practice, 1998,45(3):145-146.
[143]郭阳宽,王正林,过程控制工程及仿真[M].2009：209-211.
[144]尹先斌,周有训.基于Taylor和Pade能逼近的滞后系统IMC-PID研究[J].昆明理工大学学报(理工版),2006,31(4)：76-79.
[145]于福永.基于Pade逼近的纯滞后系统内模控制器的设计[J].苏州大学学报,2004,24(4)：26-29.
[146]刘涛,张伟东,顾延英,蔡云泽.化工多变量时滞过程的频域解耦控制设计的研究进展[J].自动化学报,2006,32(1)：73-83.
[147]M. Morari, S.S., D.E. Rivera. Implications of Internal Model Control for PID Controllers[C]. Proceedings of American Control Conference. San Diego: ACCC,1984: 661-666.
[148]齐菲.多变量内模控制的仿真研究[D].北京：北京化工大学,2008.
[149]Wood, R.K., Berry, M. W.. Terminal composition control of a binary distillation column[J]. Chemical Engineering Science,1973,28:1707-1717.
[150]王诗宓.多变量控制系统的分析和设计[M].北京：中国电力出版社,1996：1-3.
[151]潘立登,聂雪嫒,马俊英.常减压蒸馏装置监控先进控制和优化控制[J].石油化工自动化,2001,(6)：6-10.
[152]I.L. Chien, P.S.F.. Consider IMC tuning to improve performance[J]. Chem.Eng. Prog.,1990, 86(10):33-41.
[153]M. Friman, K.V.W.. Autotuning of multiloop control systems[J]. Ind. Eng.Chem. Res.,1994, 33:1708-1717.
[154]Luyben, W.L.. Tuning proportional-integral-derivative controllers for integrator/deadtime processes[J]. Ind. Eng. Chem. Res.,1996,35:3480-3483.
[155]R.C. Panda, S.-B.H., C.-C. Yu. Integrated modified Smith predictor with PID controller for integrator plus deadtime processes[J]. Ind. Eng. Chem. Res.,2006,45:1397-1407.
[156]宰文妓,汪华章.基于幅值相角裕量的改进两自由度内模控制方法[J].洛阳工业高等专科学校学报,2005,15(4)：10-12.
[157]朱宏栋,邵惠鹤.基于改进IMC的开环不稳定时滞过程控制[J].控制与决策,2005,20(7)：727-731.
[158]Tien-Li Chia, I.L., Internal model-based control for integrating processes[J]. ISA Transactions,2010, In Press.
[159]胡寿松,自动控制原理(第三版)[M].国防工业出版社,1999.
[160]M.R. Matausek, A.D.M.. A modified Smith predictor for controlling a process with an integrator and long dead-time[J]. IEEE Trans. Autom. Control,1996,41(8):1199-1203.
[161]E.I. Jury, A.G.D.. A general formulation of the total square integrals for continous systems[J]. IEEE Trans. Autom. Control,1965,10:119-120.
[162]张卫东,孙优贤,许晓鸣.非自衡对象的完全干扰解耦控制[J].上海交通大学学报,1998,32(8)：55-57.
[163]Fernando V Lima and C. Georgakis. Design of output constraints for model-based non-square controllers using interval operability[J]. Journal of Process Control,2008,18(6): 610-620.
[164]Bristol E.H.. On a new measure of interactions for multivariable process control[J]. IEEE Transactions on Automatic Control,1966,11:133-134.
[165]J.E. Hansen, S.B.J., J. Heath J.D. Perkins. Control structure selection for energy integrated distillation column[J]. J. Process Control,1998,8:185.
[166]C.D. Schaper, T.K., Y.J. Lee. Decentralized control of water temperature for multizone rapid thermal processing systems[J]. IEEE Trans. Semicond. Manufact.,1999,12:193-199.
[167]Niederlinski, A.. A heuristic approach to the design of linear multivariable interacting subsystems[J]. Automatica,1971,7:691-701.
[168]Zhu Z.X.. Stability and integrity enforcement by integrating variable pairing and controller design[J]. Chem. Eng. Sci.,1998,53:1009-1013.
[169]M. Witcher, T.J.M.. Interacting control systems:steady state and dynamic measurement of interactions[J]. ISA Trans.,1977,16:35-41.
[170]T.J. McAvoy, Y.A. R. Chen. D. Robinson P.D.. Schnelle, A new approach to defining a dynamic relative gain[J]. Control Eng. Pract.,2003,11:907-914.
[171]Xiong Q., C.W.J., He M. J.. A practical loop pairing criterion for multivariable processes[J]. Journal of Process Control,2005,15(7):741-747.
[172]Xiong Qiang, W.J.C.. Effective transfer function method for decentralized control system design of multi-input multi-output processes[J]. Journal of Process Control,2006.16: 773-784.
[173]叶琼瑜.化工过程多变量系统的PID控制研究[D].北京：北京化工大学,2008.
[174]Jing Qibing, GY., Liu Ziyi, Song Anan. Decoupling Internal Model Control for Non-square Process with Time Delays[C]. The proceedings of 2010 International Conference on Measuring Technology and Mechatronics Automation,2010:898-901.
[175]Valchos C., Williams D., Gomm J. B. Solution to the shell control problem using genetically tuned PID controllers[J]. Control Engineering Practice,2002,10:151-163.
[176]A. Seshagiri, M.C.. Smith delay compensator for multivariable non-square systems with multiple time delays[J]. Computer and Chemical Engineering,2006,30(8):1243-1255.
[177]吴国垣,李东海,薛亚丽,唐多元.多变量系统分散PID控制器设计[J].清华大学学报(自然科学版),2004,44(11)：1567-1570.
[178]Guan Tien Tan, M.S.C.. A multiple-model approach to decentralized internal model control design[J]. Chemical Engineering Science,2001,56:6651-6660.
[179]Loh A.P., Vasnani V. U.. Describing function matrix for multivariable systems and its use in multiloopPI design[J], Journal of Process Control,1994,4(3):115-120.
[180]周平,柴天佑,陈通文.工业过程运行的解耦内模控制方法[J].自动化学报,2009,35(10)：1362-1368.
[181]要艳静,王晶,潘立登.多变量多时滞非方系统的解耦内模控制[J].化工学报,2008,59(7)：1737-1742.
[182]刘慧,袁文燕,姜冬青.矩阵论及应用[M].北京：化学工业出版社,2003.
[183]Normey-Rico, J.E., Bordon C., Camacho, E. F., Improving the robustness of dead time compensating PI controllers[J]. Control Engineering Practice,1997,5:801-810.
[184]靳其兵,刘斯文,权玲,曹丽婷.基于新型解耦内模控制方法及在非方系统中的应用[J].自动化学报(发表中)
[185]龚纯,王正林.精通MATLAB最优化计算[M].电子工业出版社,2009.
[2]刘子宜.系统辨识方法的研究与工程化应用[D].北京：北京化工大学,2009.
[3]蒋文春.过程辨识方法的研究及实现[D].北京：北京化工大学,2009.
[4]A. SESHAGIRI, R.A.M., CHIDAMBARAM. Decoupled Smith predictor for multivariable nonsquare systems with multiple time delays[J]. Indian Institute of Science,2006,86: 235-256.
[5]潘峰,韩如成.时变大时滞系统的控制方法综述[J].仪器仪表学报,2002,23(3)：789-91.
[6]刘明鑫.时滞系统控制方法的研究[D].北京：北京化工大学,2009.
[7]陈娟.复杂系统内模控制策略的研究与实现[D].北京：北京化工大学,2006.
[8]Layton J.M.. Multivariable control theory [M]. stevenage, England: Peter Peregrines for the Institution of Electrical Engineers,1976.
[9]Maciejowski J.M.. Multivariable feedback design[M]. Workingham, England: Addison-Wesley,1989.
[10]宰文妓.一类非自衡对象的改进两自由度内模控制[D].四川：四川大学,2006.
[11]Garcia C E, M.M.. Internal model control.1.A unifying review and some new Results[J]. Industrial Engineering Chemistry Process Design and Development,1982.21(2):308-323.
[12]Richalet J A. Model predictive heuristic control:applications to an industrial Process[J]. Automatica,1978,14(5):413-428.
[13]Cutler C. R., R.L.B.. Dynamic Matrix Control-a Computer Control Algorithm.[J]. In: JACC P roceedings,1980:W P5-B.
[14]赵曜.内模控制发展综述[]].信息与控制,2000,29(6)：526-531.
[15]冯春蕾.内模控制技术及其在多变量时滞系统中的应用[D].北京：北京化工大学,2009.
[16]黄静,基于内模控制的PID控制系统的研究与应用[D].北京：北京化工大学,2006.
[17]Garcia C E, M. M.. Internal Model Control.2. Design procedure for multivariable System[J]. I&EC Process Des.Dev,1985,24(2):472-484.
[18]Garcia C E, M.M. Internal model control.3.Multivariable control law computation and tuning guidelines[J]. Industrial Engineering Chemistry Process Design and Development, 1985,24(2):484-494.
[19]Morari M and Z. E., Robust Process control[M]. Prentice-Hall,1988.
[20]薛兴昌.工业自动化系统的技术发展[J].自动化博览,2003(8)：48-50.
[21]Garcia C E, M.M.. Internal Model Control-Extention to Nonlinear System[J]. Ind. Eng. Chem. Proc. Des. Dev.,1986,25(2):403-411.
[22]Economou C G, M.M.. Internal model control:6 multiloop design[J]. Ind. Eng. Chem. Proc. Des.Dev.,1986,25:411-419.
[23]袁琴,多变量系统内模控制方法研究[D].北京：北京化工大学,2008.
[24]郑恩让,张玲.内模控制在造纸生产定量控制中的应用[J].化工自动化及仪表,2001,28(3)：23-24.
[25]王东风,王剑东,韩璞.一种多变量系统的内幕解耦控制设计方法[J].控制工程,2003,10(5)：463-465.
[26]程志强,戴连奎,孙优贤.加热炉热效率软测量与内模控制[J].信息与控制,2004,33(1)：85-88.
[27]杨锡运,徐大平,张彬.再热汽温系统的内模解藕控制[J].动力工程,2004,24(24)：529-532.
[28]Wang Qingguo, Z.Y., Chiu Minsen.. Decoupling internal model control for multivariable systems with multiple time delays[J]. Chemical Engineering Science,2002,57:115-124.
[29]Edgar C R, P.B.E.. MIMO fuzzy internal model control[J]. Automatica,2000,34:867-877.
[30]Zheng A., Kothare M V., and M. M.. Anti-windup design for internal model control[J]. Int J Control,1994,60(5):1015-1024.
[31]Sairam V, M.S., Input constraint handling and windup compensation in nonlinear control[J]. Proc of American Control Conference,1997:1734-1738.
[32]Malan S, M.E.A., Regruto D.. Robust control from data via uncertainty model sets identification[C]. Proc of the 40th IEEE Conf on Decision and Control Orlando: IEEE Press, 2001,3:2686-2691.
[33]Campi M., L.W.S., Anaerson B. D. O.. Filters for internal model control design for unstable plants[C]. Proc of 32 nd IEEE Conf on Decision and Control. San Antonio: IEEE Press, 1993:1343-1348.
[34]Rivals I., P.L.. Nonlinear internal model control using neural networks: application to processes with delay and design issues[J]. IEEE Trans on Neural Networks,2000,11(1): 80-90.
[35]王建国.时滞系统增益自适应内模PID控制方法[J].吉林大学学报(信息科学版),2004,22(4)：355-358.
[36]华建兴,席裕庚.带预估控制量二次约束的无静差FIR型内模控制器[J].控制理论与应用,1999,16(4)：492-495.
[37]潘立登,潘仰东.系统辨识与建模[M].北京：化学工业出版社,2004.
[38]莫建林,王伟,许晓鸣,张卫东.系统辨识中的闭环问题[J].控制理论与应用,2002,19(1)：9-14.
[39]李少远,蔡文剑.工业过程辨识与控制[M].北京：化学工业出版社,2005：206-214.
[40]温之建.多变量系统辨识的研究与应用[D].北京：北京化工大学,2003.
[41]夏天长.系统辨识-最小二乘法[M].熊光楞,李芳芸译.北京：清华大学出版社,1983.
[42]王永,李旺,梁青.多变量频域极大似然辨识算法研究[J].东南大学学报(自然科学版),2005,35(增刊Ⅱ)：15-21.
[43]李旭,高升,熊忠辉.一类多变量系统特殊矩形波响应在线辨识[J].动力工程,2004,24(1)：95-97.
[44]Luss R, J.T.H.I.. Optimization by Direct search and Systematic Reduction of the Size of Search Region[J]. AIChE,1973,19(4):760-765.
[45]焦嵩鸣.人工神经网络在系统辨识中的研究与应用[M].保定：华北电力大学,2002.
[46]齐建玲.一种改进的BP网络系统辨识算法的研究[J].华北航天工业学院学报,2006,16(2)：1-5.
[47]舒华,舒怀林.基于PID神经网络的多变量非线性动态系统辨识[J].计算机工程与应用,2006,12：47-49.
[48]Olufemi A. Adebiyi, A.B.C.. Dynamic neural networks partial least squares (DNNPLS) identification of multivariable processes[J]. Computers and Chemical Engineering,2003, 27:143-155.
[49]刘树安,唐非.基于遗传算法的系统辨识方法研究[J].系统工程理论与实践,2007,3：134-139.
[50]任子武,伞冶.自适应遗传算法的改进及在系统辨识中应用研究[J].系统仿真学报,2006,18(1)：41-43.
[51]徐洪泽,褚东升,张福恩.一种改进的遗传算法及其在系统辨识中的应用[J].哈尔滨工业大学学报,1997,29(4)：72-75.
[52]王月鹃,万百五.大时延过程的控制方法[J].控制理论与应用,1986,3(3)：1-12.
[53]欧林林,顾诞英,张卫东.基于幅值裕度和相位裕度的PID参数最优整定方法[J].控制理论与应用,2007,24(5)：837-840.
[54]张静,曹玉强,王强.大时滞过程的自整定PID控制器[J].测控技术,2006,25(3)：63-66.
[55]曹刚,俞海斌,徐巍华等.大时滞不稳定对象的PID控制[J].仪器仪表学报,2005,26(3)：301-303,324.
[56]严兴华,王钦若,曾永荣IMC-PID在大惯性大滞后系统中的应用及仿真[J].广东有色金属学报,2006,13(3)：217-220.
[57]褚丽丽,李春茂,郭汉桥,刘军华.时滞系统的模糊自适应PID控制研究[J].自动化技术与应用,2008,1：34-36.
[58]胡品慧,徐志前,闫峰等.滞后过程控制方法研究[V].第二十二届中国控制会议论文集,2003：277-279.
[59]王树青,金晓明,荣冈.工业生产过程的先进控制[J].化工自动化及仪表,1999,26(2)：61-65.
[60]Dahlin E.B.. Design and tuning digital controller[J]. Instr.& Contr.Sys,1968,41(7):77-83.
[61]刘金琨.先进PID控制MATLAB仿真(第2版)[M].电子工业出版社,2004.
[62]朱学峰.大林算法与Smith预估器[J].自动化与仪器仪表,1999,20(4)：122-124.
[63]胡国龙,孙优贤.预测控制进展及其应用研究[J].电力系统及其自动化学报,2003,15(1)：109-114.
[64]刘明鑫.时滞系统控制方法的研究[D].北京：北京化工大学,2009.
[65]陈娟,陈岳峰,潘立登,曹柳林.多变量时滞系统的解耦模糊内模控制[J].电机与控制学报,2006,10(2)：203-207.
[66]靳其兵,宋洪法,路香菊.大滞后系统数字内模控制器的设计及仿真研究[J].计算机仿真,2005,22(8)：151-153.
[67]孙优贤,褚键.工业过程控制技术[M].化学工业出版社,2006.
[68]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.
[69]Wu ZhiQiao, M.M.. PID Type Fuzzy and Parameters Adaptive Method[J]. Fuzzy Sets and Systems,1996,78:23-25.
[70]Kevin M Passino, S.Y.. Fuzzy Control[M]. Tsinghua University Press,2001.
[71]姚继兵,吴玲,胡学妹.一种基于遗传神经网络的模糊控制系统[J].四川大学学报,2001,33(3)：117-119.
[72]K. S. Narendra, K.P.. Identification and control of dynamical systems using neural networks[J]. IEEE Transactions on Neural Networks,1990, (1):4-24.
[73]GuillermoJ.Silva, Aniruddha Datta. S.P.B.. New results on the synthesis of PID controller[J]. Control Theory and Applieations,2002,151(1):89-94.
[74]张卫东,孙优贤,许晓鸣.一类非自衡对象的PID控制[J].自动化学报,1999,25(4)： 518-523.
[75]张井刚,刘志远,裴润.一类非自衡对象的二自由度PID控制[J].控制与决策,2002,176：886-889.
[76]Yongli Song, M.O.T., Tonghua Zhang. Stabilization and algorithm of integrator plusdead-time process using PID controlled[J]. Journal of Process Control,2009,19: 1529-1537.
[77]M. Chidambaram, R.P.S.. A simple method of tuning PID controllers for integrator/dead-time processes[J]. Computers and Chemical Engineering 2003,27:211-215.
[78]Jeffrey E. Arbogast, D.J.C.. Extension of IMC tuning correlations for non-self regulating (integrating) processes. ISA Transactions,2007,46:303-311.
[79]Chien I., P.F.. Consider IMC tuning to improve controller performance[J]. Chemistry Engineering Program,1990,10:33-41.
[80]K.J. Astrom, C.C. Hang, and B.C. Lim. A new Smith predictor for controlling a process with an integrator and long dead time. IEEE Trans. Autom. Control,1994,39(2):343-345.
[81]Xiang Lu, Y.S., Qing Guowang, Wei Xingzheng. A double two-degree-of-freedom control scheme for improved control of unstable delay processes[J]. Journal of Process Control, 2005,15:605-614.
[82]Zhang Q.C., N.R.J.E., Control of integral proeesses with dead time-1:Disturbance observer-based 2DOF control seheme[J]. IEE Proe Control Theory,2002,149(4):285-290.
[83]Tien-Li Chia, I.L.. Internal model-based control for integrating processes[J]. ISA Transactions,2010:In press.
[84]Xiu zhang Jin, J.Z.S., Ji Zheng Zhang.. Analysis and design of adaptive internal model control for unstable proeess with deadtime[C]. Proceedings of the Third International Conference on Machine Learning and Cyberneties,2004,8:983-987.
[85]宰文妓,赵曜,汪华章.一类非自衡对象的两自由度内模控制[J].四川大学学报,2006,43(3)：561-564.
[86]Gareia C.E., M.M.. State-space interpretation of model predietive control[J]. Automatica, 1994,30(4):707-717.
[87]邹涛,刘红波,李少远.锅炉汽包水位非自衡系统的预测控制[J].控制理论与应用,2004,21(3)：386-390.
[88]回立川,陈蓓,郑恩让.系统的模糊-Smith预测控制[J].微计算机信息,2005,9(1)：38-40.
[89]K. L. N. Sarma, C.M.. Centralized PI/PID controllers for nonsquare systems with RHP zero[J]. J of Indian Institute Science,2005,85(4):201-214.
[90]郭妍.非非方系统内模控制方法的研究[D].北京：北京化工大学,2010.
[91]Davison E.. Some properties of minimum phase systems and" Squared-down" systems[J]. IEEE Transactions on Automatic Control,1983,2:221-222.
[92]Treiber S.. Multivariable control of non-square systems[J]. Industrial & Engineering Chemistry Process Design and Development,1984(4):854-857.
[93]D.E. Reeves, A.Y., Interaction measures for nonsquare decentralized control structures[J]. AICHE,1989(35):603-613
[94]Jin Wen Chang, C.Y., The relative gain for non-square multivariable systems[J]. Chem. Eng. Sci.,1990,45(5):1309-1323.
[95]Isidori, A.. Nonlinear Control Systems: an Introduction Springer Verlag. New York. NY, 1989.
[96]McLain R. B., K.M.J., Henson M. A.,et al. Habituating control for non-square nonlinear processes[J]. Ind. Eng. Chem. Res,1996,35(11):4067-4077.
[97]赵大力.工业过程的先进控制策略研究[D].北京：北京化工大学,2008.
[98]Morari M., GW., Oglesby M., Prosser I.. Design of resilient processing plants. Ⅶ: Design of energy management system for unstable reactors-new insights[J]. Chemical Engineering Science,1985(2):187-198.
[99]Chiu E.J.L.a.M.S.. Robust decentralized control of nonsquare systems[J]. Chem. Engng Commun,1997,22:157-180.
[100]Wang Q., Z.B., Zhang Y.. Decoupling Smith predictor design for multivariable systems with multiple time delays[J]. Chemical Engineering Research and Design,2000, (4):565-572.
[101]Seshagiri A, C.M.. Smith delay compensator for multivariable non-square system with multiple time delays[J]. Computer and Chemical Engineering,2006,.30:1243-1255.
[102]萧明波,钱积新.预测控制中静态目标的实现[J].控制理论与应用,1997,14(3)：313-317.
[103]陈莉.非方广义系统带最坏干扰抑制的奇异LQ问题[J].高校应用数学学报,2007.22(1)：9-16.
[104]D.R. VIinson, C.G..A new measure of process output controllability[J]. Proceedings of the 1998 IFAC International Symposium on Dynamics and Control of Process Systems (DYCOPS),1998,2:700-709.
[105]D. R. Vinson, C.G.. A new measure of process output controllability[J]. Journal of Process Control,2000,10(2):185-194.
[106]P. Suganda, P.R. Krishnaswamy, and G.P. Rangaiah. On-Line Process Identification from Closed-Loop Tests Under PI Control[J]. Chemical Engineering Research and Design,1998, 76:451-457.
[107]潘立登.最优化在在线调节器整定中的应用[J].北京化工学院学报,1984(2)：34-36.
[108]U. Baur, R.I.. On-line identification of a heat exchanger with a process computer-A case study[J]. Automatica,1977, (13):487-496.
[109]Jyh Horng Chou, J.-H.S.. Jyh-Nan Shieh, On-line identification and optimal control of continuous-time systems[J]. Mathematics and Computers in Simulation,2003,63:493-503.
[110]Karim Salahshoor, M.H.. A novel online affine model identification of multivariable processes using adaptive neuro-fuzzy networks[J]. Chemical Engineering Research and Design,2010,8:155-169.
[111]张磊.时滞系统的辨识与控制[D].北京：北京化工大学,2007.
[112]Gauss C.F.. Theoria Motus Corporum Coelestium in sectionibus conicis solem ambientium[M]. Hamburg, F. Perthes and Besser,1809.
[113]L.Miskovic, A.K., D. Bonvin, M. Gevers. Closed-loop identification of multivariable systems: With or without excitation of all references?[J] Automatica,2008,44:2048-2056.
[114]Holland J.H.. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence[M]. University of Michigan Press,1992.
[115]Timothy I.Salsbury. Continuous-time model identification for closed loop control performance assessment[J]. Control Engineering Practice,2007,15:109-121.
[116]Kennedy J. and E.R. C.. Particle swarm optimization[C]. Proceedings of IEEE international Conference on Neural Networks.Piscataway,IEEE Press,1995,4:1942-1948.
[117]Eberhart R. C and K. J.. A new optimizer using particle swarm theory[C]. Proceedings of the Sixth International Sympisium on Micro Machine and Human Science. IEEE Press, Nagoya, Japan.,1995:39-43.
[118]Kennedy J., E.R.C., Shi,Y.. Swarm intelligence.[M] San Francisco: Morgan Kaufmann Publishers,2001.
[119]Kennedy J. and E.R. C, A discrete binary version of the particle swarm algorithm[C]. Proceeding of the IEEE Conference on Systems, Man and Cybernetics SMC, IEEE Press, 1997,5:4104-4108.
[120]朱志斌,张可村.不等式约束优化一个新的SQP算法[J].计算数学,2004.
[121]马俊英,罗元浩,潘立登.用改进的NLJ方法辨识闭环系统的模型参数及滤波器设计[J].北京化工大学学报,2003,30(4)：95-97.
[122]T.C.Hsia系统辨识与应用[M].长沙：中南工业大学出版社,1986.
[123]孙晓天.单变量过程辨识方法及内模控制的研究与应用[D].北京：北京化工大学,2010.
[124]Zadeh L.A., From Circuit Theory to System Theory[D]. Colorado State University,1962.
[125]L.Ljung. Asymptotic variance expressions for identified black-box transfer function models [J]. IEEE Trans. Auto. Control,1985, AC-30:834-844.
[126]L.Lunng. System Identification-Theory for the user, Prentice Hall[J]. Englewood Cliffs, NJ, 1999.
[127]潘立登.先进控制与在线优化技术及其应用[M].机械工业出版社,2009.
[128]潘立登.LJ最优化方法的改进[J].化工自动化及仪表,1985,12：10-15.
[129]吕振肃,侯志荣.自适应变异的粒子群优化算法[J].电子学报,2004,32(3)：416-419.
[130]Sun J., X.W.B.. A global search strategy of quantum-behaved particle swarm optimization[C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems:IEEE Press,2004:111-116.
[131]M.J.D.Powell. An Efficient Method for Finding the Minimum of a Function of Several Variables without Calculating;Derivatives[J]. The Computer Journal 1964,7(2):155-162
[132]宗伟新,慕福顺,李晋惠.数字化自动控制系统性能指标的测量方法[J].国外电子测量技术,2006,10：40-41.
[133]王树青,先进控制技术及应用[M].化学工业出版社,2001.
[134]Francis B.A., W.W.M.. The internal model principle for linear multivariable regulators[J]. AppIMath and Opt,1975,2:170-194.
[135]宋云霞,朱学峰.大时滞过程控制方法及应用[J].化工自动化及仪表,2001,28(4)：9-15.
[136]赵曜.基于内模结构的鲁棒有限拍控制极其预测控制的比较[C].1995中国控制会议论文集,武汉：中国科学技术出版社,1995：157-162.
[137]刘晨晖.多变量过程控制系统解耦理论[M].北京：水利电力出版社,1984.
[138]Y. S. Apte. Linear Multivariable control theory [M]. London: McGraw Hill Book Company, 1981.
[139]Liu C H, General Decoupling theory of multivariable process control system[M].1983, Berlin: Springer-Verlag.
[140]周彦,时变大滞后过程仿人智能模糊控制[D].广西：广西大学,2006.
[141]蒋慰孙,叶银忠,多变量控制系统分析与设计[M].北京：中国石化出版社,1997：74-92.
[142]JohnGerry. How to control process with large dead time[J]. Control Engineering Practice, 1998,45(3):145-146.
[143]郭阳宽,王正林,过程控制工程及仿真[M].2009：209-211.
[144]尹先斌,周有训.基于Taylor和Pade能逼近的滞后系统IMC-PID研究[J].昆明理工大学学报(理工版),2006,31(4)：76-79.
[145]于福永.基于Pade逼近的纯滞后系统内模控制器的设计[J].苏州大学学报,2004,24(4)：26-29.
[146]刘涛,张伟东,顾延英,蔡云泽.化工多变量时滞过程的频域解耦控制设计的研究进展[J].自动化学报,2006,32(1)：73-83.
[147]M. Morari, S.S., D.E. Rivera. Implications of Internal Model Control for PID Controllers[C]. Proceedings of American Control Conference. San Diego: ACCC,1984: 661-666.
[148]齐菲.多变量内模控制的仿真研究[D].北京：北京化工大学,2008.
[149]Wood, R.K., Berry, M. W.. Terminal composition control of a binary distillation column[J]. Chemical Engineering Science,1973,28:1707-1717.
[150]王诗宓.多变量控制系统的分析和设计[M].北京：中国电力出版社,1996：1-3.
[151]潘立登,聂雪嫒,马俊英.常减压蒸馏装置监控先进控制和优化控制[J].石油化工自动化,2001,(6)：6-10.
[152]I.L. Chien, P.S.F.. Consider IMC tuning to improve performance[J]. Chem.Eng. Prog.,1990, 86(10):33-41.
[153]M. Friman, K.V.W.. Autotuning of multiloop control systems[J]. Ind. Eng.Chem. Res.,1994, 33:1708-1717.
[154]Luyben, W.L.. Tuning proportional-integral-derivative controllers for integrator/deadtime processes[J]. Ind. Eng. Chem. Res.,1996,35:3480-3483.
[155]R.C. Panda, S.-B.H., C.-C. Yu. Integrated modified Smith predictor with PID controller for integrator plus deadtime processes[J]. Ind. Eng. Chem. Res.,2006,45:1397-1407.
[156]宰文妓,汪华章.基于幅值相角裕量的改进两自由度内模控制方法[J].洛阳工业高等专科学校学报,2005,15(4)：10-12.
[157]朱宏栋,邵惠鹤.基于改进IMC的开环不稳定时滞过程控制[J].控制与决策,2005,20(7)：727-731.
[158]Tien-Li Chia, I.L., Internal model-based control for integrating processes[J]. ISA Transactions,2010, In Press.
[159]胡寿松,自动控制原理(第三版)[M].国防工业出版社,1999.
[160]M.R. Matausek, A.D.M.. A modified Smith predictor for controlling a process with an integrator and long dead-time[J]. IEEE Trans. Autom. Control,1996,41(8):1199-1203.
[161]E.I. Jury, A.G.D.. A general formulation of the total square integrals for continous systems[J]. IEEE Trans. Autom. Control,1965,10:119-120.
[162]张卫东,孙优贤,许晓鸣.非自衡对象的完全干扰解耦控制[J].上海交通大学学报,1998,32(8)：55-57.
[163]Fernando V Lima and C. Georgakis. Design of output constraints for model-based non-square controllers using interval operability[J]. Journal of Process Control,2008,18(6): 610-620.
[164]Bristol E.H.. On a new measure of interactions for multivariable process control[J]. IEEE Transactions on Automatic Control,1966,11:133-134.
[165]J.E. Hansen, S.B.J., J. Heath J.D. Perkins. Control structure selection for energy integrated distillation column[J]. J. Process Control,1998,8:185.
[166]C.D. Schaper, T.K., Y.J. Lee. Decentralized control of water temperature for multizone rapid thermal processing systems[J]. IEEE Trans. Semicond. Manufact.,1999,12:193-199.
[167]Niederlinski, A.. A heuristic approach to the design of linear multivariable interacting subsystems[J]. Automatica,1971,7:691-701.
[168]Zhu Z.X.. Stability and integrity enforcement by integrating variable pairing and controller design[J]. Chem. Eng. Sci.,1998,53:1009-1013.
[169]M. Witcher, T.J.M.. Interacting control systems:steady state and dynamic measurement of interactions[J]. ISA Trans.,1977,16:35-41.
[170]T.J. McAvoy, Y.A. R. Chen. D. Robinson P.D.. Schnelle, A new approach to defining a dynamic relative gain[J]. Control Eng. Pract.,2003,11:907-914.
[171]Xiong Q., C.W.J., He M. J.. A practical loop pairing criterion for multivariable processes[J]. Journal of Process Control,2005,15(7):741-747.
[172]Xiong Qiang, W.J.C.. Effective transfer function method for decentralized control system design of multi-input multi-output processes[J]. Journal of Process Control,2006.16: 773-784.
[173]叶琼瑜.化工过程多变量系统的PID控制研究[D].北京：北京化工大学,2008.
[174]Jing Qibing, GY., Liu Ziyi, Song Anan. Decoupling Internal Model Control for Non-square Process with Time Delays[C]. The proceedings of 2010 International Conference on Measuring Technology and Mechatronics Automation,2010:898-901.
[175]Valchos C., Williams D., Gomm J. B. Solution to the shell control problem using genetically tuned PID controllers[J]. Control Engineering Practice,2002,10:151-163.
[176]A. Seshagiri, M.C.. Smith delay compensator for multivariable non-square systems with multiple time delays[J]. Computer and Chemical Engineering,2006,30(8):1243-1255.
[177]吴国垣,李东海,薛亚丽,唐多元.多变量系统分散PID控制器设计[J].清华大学学报(自然科学版),2004,44(11)：1567-1570.
[178]Guan Tien Tan, M.S.C.. A multiple-model approach to decentralized internal model control design[J]. Chemical Engineering Science,2001,56:6651-6660.
[179]Loh A.P., Vasnani V. U.. Describing function matrix for multivariable systems and its use in multiloopPI design[J], Journal of Process Control,1994,4(3):115-120.
[180]周平,柴天佑,陈通文.工业过程运行的解耦内模控制方法[J].自动化学报,2009,35(10)：1362-1368.
[181]要艳静,王晶,潘立登.多变量多时滞非方系统的解耦内模控制[J].化工学报,2008,59(7)：1737-1742.
[182]刘慧,袁文燕,姜冬青.矩阵论及应用[M].北京：化学工业出版社,2003.
[183]Normey-Rico, J.E., Bordon C., Camacho, E. F., Improving the robustness of dead time compensating PI controllers[J]. Control Engineering Practice,1997,5:801-810.
[184]靳其兵,刘斯文,权玲,曹丽婷.基于新型解耦内模控制方法及在非方系统中的应用[J].自动化学报(发表中)
[185]龚纯,王正林.精通MATLAB最优化计算[M].电子工业出版社,2009.