时滞过程的闭环辨识与先进控制策略的仿真与应用研究
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摘要
时滞现象普遍存在于工业生产过程中,本文所研究的两个对象,治疗膀胱癌的热疗仪实验装置和水厂加药絮凝过程都是具有明显纯滞后的过程,尤其是水厂的加药絮凝过程,其纯滞后时间与时间常数之比甚至超过了1。时滞的存在会对控制系统产生不利的影响,特别是对于大时滞过程,常规的PID控制器很难取得理想的控制效果,甚至会导致系统的不稳定。因此,有必要针对大时滞过程设计先进的控制策略,而了解过程的对象模型对于控制策略的设计是有极大帮助的。
本文首先提出了一种新的基于PI控制的闭环辨识方法,该方法是假定对象为二阶加时滞(SOPTD)过程,用PI控制器使得闭环响应呈欠阻尼状态,根据欠阻尼的闭环响应曲线,首先得到闭环传递函数,然后通过等效法和比较系数法将PI控制器从闭环传递函数中分离出来,反推出对象的模型。将此辨识方法用于热疗仪实验装置中,取得较好的辨识效果。
然后针对水厂加药絮凝过程,设计了基于内模控制的PID控制器(IMC_PID),比较了IMC_PID控制器与Lopez法和Z-N法整定的PID控制器的控制效果,仿真结果表明IMC_PID具有结构简单,利于在线整定的优点。之后在IMC_PID的基础上进行了二自由度IMC_PID的仿真研究,表明了二自由度IMC_PID能够较好的兼顾快速性和鲁棒性。
接下来采用改进的差分进化算法,以一种能抗超调的MITAE指标为优化目标函数,进行PID参数的优化,结果表明MITAE指标达到了预期的目标。
本文最后采用了美国通控集团博软分部的一款基于无模型自适应控制技术的控制器(MFA)对水厂的加药絮凝过程进行了前馈-反馈控制系统的设计,并在佛山某水厂的中试装置中得到应用,取得了良好的控制效果。
Lag is commonly found in industrial processes. This paper studies two objects, the hyperthermia instrument experimental device used for the treatment of bladder cancer and the dosing flocculation process of the water plant are both processes with large time-delay. Especially for the dosing flocculation process, the ratio of the pure delay-time and the time constant is even more than one. The existence of time-delay would have a negative impact on the control system. Especially for large time-delay process, the conventional PID controller is difficult to achieve the desired response, or even badly lead to instability. Therefore, it is necessary for the large time-delay process design of advanced control technologies. However the advanced control technologies are based on the process model as the basis for design.
First, this paper presents a new closed-loop identification based on PI control method, which assumes that the process for the second-order plus time delay (SOPTD) model. It makes the closed-loop response was underdamped with the PI controller, then the PI closed-loop transfer function with SOPDT model can be approximatively obtained through the closed-loop underdamped step response. Then the open-loop transfer function of the process can be obtained by the equivalent transforming of block diagram to separate the proportional-integral controller from the closed-loop transfer function. At last, the SOPDT model of the process can be attained by coefficient of comparison. This identification method is used in the hyperthermia instrument experimental device to obtain a better identification results.
Then, a PID controller based on the internal model control(IMC_PID) is designed for the dosing flocculation process. And it compares the IMC_PID controller with controller tuning by Lopez and Z-N law. Simulation results show that the IMC_PID controller has the advantages of simple structure and easily tuning on-line. And then the simulation studies of a two degrees of freedom IMC_PID show that the two degrees of freedom IMC_PID can be better able to balance the speediness and robustness.
Next, it uses an improved differential evolution algorithm to optimize the PID parameter with the objective function of an anti-overshoot MITAE index. The results prove that MITAE index achieve the desired target.
At last, it designs a feedforward-feedback control system based on the model free adaptive(MFA) controller of the Cybosoft. The system has been applied in the dosing flocculation process of a pilot plant in a water plant in Foshan. And it achieved a good control effect.
本文首先提出了一种新的基于PI控制的闭环辨识方法,该方法是假定对象为二阶加时滞(SOPTD)过程,用PI控制器使得闭环响应呈欠阻尼状态,根据欠阻尼的闭环响应曲线,首先得到闭环传递函数,然后通过等效法和比较系数法将PI控制器从闭环传递函数中分离出来,反推出对象的模型。将此辨识方法用于热疗仪实验装置中,取得较好的辨识效果。
然后针对水厂加药絮凝过程,设计了基于内模控制的PID控制器(IMC_PID),比较了IMC_PID控制器与Lopez法和Z-N法整定的PID控制器的控制效果,仿真结果表明IMC_PID具有结构简单,利于在线整定的优点。之后在IMC_PID的基础上进行了二自由度IMC_PID的仿真研究,表明了二自由度IMC_PID能够较好的兼顾快速性和鲁棒性。
接下来采用改进的差分进化算法,以一种能抗超调的MITAE指标为优化目标函数,进行PID参数的优化,结果表明MITAE指标达到了预期的目标。
本文最后采用了美国通控集团博软分部的一款基于无模型自适应控制技术的控制器(MFA)对水厂的加药絮凝过程进行了前馈-反馈控制系统的设计,并在佛山某水厂的中试装置中得到应用,取得了良好的控制效果。
Lag is commonly found in industrial processes. This paper studies two objects, the hyperthermia instrument experimental device used for the treatment of bladder cancer and the dosing flocculation process of the water plant are both processes with large time-delay. Especially for the dosing flocculation process, the ratio of the pure delay-time and the time constant is even more than one. The existence of time-delay would have a negative impact on the control system. Especially for large time-delay process, the conventional PID controller is difficult to achieve the desired response, or even badly lead to instability. Therefore, it is necessary for the large time-delay process design of advanced control technologies. However the advanced control technologies are based on the process model as the basis for design.
First, this paper presents a new closed-loop identification based on PI control method, which assumes that the process for the second-order plus time delay (SOPTD) model. It makes the closed-loop response was underdamped with the PI controller, then the PI closed-loop transfer function with SOPDT model can be approximatively obtained through the closed-loop underdamped step response. Then the open-loop transfer function of the process can be obtained by the equivalent transforming of block diagram to separate the proportional-integral controller from the closed-loop transfer function. At last, the SOPDT model of the process can be attained by coefficient of comparison. This identification method is used in the hyperthermia instrument experimental device to obtain a better identification results.
Then, a PID controller based on the internal model control(IMC_PID) is designed for the dosing flocculation process. And it compares the IMC_PID controller with controller tuning by Lopez and Z-N law. Simulation results show that the IMC_PID controller has the advantages of simple structure and easily tuning on-line. And then the simulation studies of a two degrees of freedom IMC_PID show that the two degrees of freedom IMC_PID can be better able to balance the speediness and robustness.
Next, it uses an improved differential evolution algorithm to optimize the PID parameter with the objective function of an anti-overshoot MITAE index. The results prove that MITAE index achieve the desired target.
At last, it designs a feedforward-feedback control system based on the model free adaptive(MFA) controller of the Cybosoft. The system has been applied in the dosing flocculation process of a pilot plant in a water plant in Foshan. And it achieved a good control effect.
引文
[1]王树青等.先进控制技术及其应用[M].北京:化学工业出版社, 2001, 7:115
[2]何衍庆等.工业生产过程控制[M].北京:化学工业出版社, 2004, 2: 178
[3] Van den Hof P M J , Schrama R J P. An indirect method for transfer function estimation from closed - loop data [J] .Automatica ,1993 ,29 (6) :1523– 1527
[4]曹江涛,李平,郭丹.两阶段闭环辨识算法仿真[J].石油化工高等学校学报, 2003, 16(3): 70-74
[5]曹江涛,李平,姬晓飞.两阶段辨识算法在加热炉控制中的应用[J].工业仪表与自动化装置, 2004, (1): 14-17
[6]李超,黄德先,金以慧.有滞后环节的系统的闭环鲁棒辨识方法[J].化工自动化与仪表, 2003, 30(6): 11-15
[7]晓龙,陈政石,刘美.基于Laguerre函数预测控制模型的仿真智能控制[J].茂名学院学报, 2008, 18(6): 41-4
[8]夏伯锴,袁璞.基于Laguerre函数模型的大纯滞后系统预测控制[J].石油大学学报, 2002, 26(6): 100-112
[9]王亚刚,许晓鸣.过程对象模型在线闭环频域辨识[J].微计算机信息, 2009, 25(4-1): 282-283, 291
[10] Wang Xuelei and Shao Huihe. A phase-relevant closed-loop frequency-domain identification method and its application in optimal tuning of PI controllers[J].仪器仪表学报(英文), 2002, 23(5): 441-445
[11] LuusR, Jaakola T.H.I. Optimization by Direct Search and Systematic Reduction of the Size of Search Region[J].AIChE, 1973, 19:760-766
[12]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
[13] Yuwana, M. and Seborg, D.E. , A new method for on-line controller tuning[J]. Aiche Journal 1982, 28(3): 434-440
[14] Arthur Jutan and E. S. Rodrigues. Extension of a new method for on-line controller tuning[J]. The Canadian Journal of Chemical Engineering, Volume 62, Issue 6(802-807)
[15]刘恩国,俞金寿.单闭环测试辨识和在线PID参数整定[J].炼油化工自动化, 1992, (6): 23-29
[16] R. Manat, P.J. Fleming. Method for on-line identification of a first order plus dead-time process model[J]. ELECTRONICS LETTERS, 1995, 31(15): 1297-1298
[17]陈永云,刘爱伦,陈成良.一种PI闭环辨识新方法[J].控制工程, 2002, 9(6): 82-83, 96
[18]陈永云,刘爱伦.一种PID条件下的闭环辨识方法[J].石油化工自动化, 2004, (3): 31-32, 48
[19] Wonhui Cho, Thomas F. Edgar and Jietae Lee. Closed-loop identification of wafer temperature dynamics in a rapid thermal process[J]. Korean Journal of Chemical Engineering, 2006, 23(2): 171-175
[20] Smith O.J.M. Close control of loops with dead time[J]. Chemical Engineering Progress Trans, 1957. 53(5): 17-219
[21]克忠,郝中恕.计算机控制系统分析与设计[M].北京:清华大学出版社, 1988
[22] Hang CC. Performance study of Control Systemwith Dead Time[J]. IEEE. IECI, 1980, 27: 234-241
[23] Watanabe K. A process-model control for linear system with delay[J]. IEEE Trans AC-26, 1981, (6): 1261-1269
[24]蒋爱平,俞金寿.改进结构的Smith预估器[J].石油化工自动化. 1998, 22(3): 22-28
[25] Giles R.f,Bartley T M.Gain-adaptive dead time compensation ISA[J]. Trans, 1977, (1): 46-49
[26]王连铮,张建华,孟庆贤.增益预估自适应纯滞后补偿控制器[J].自动化学报, 1997, 23(1): 139-142
[27]李遵基,袁应钦,段志刚.主汽温自适应预估控制方法的研究[J].华北电力大学学报, 1999, 23(4)
[28]汪秉文,李国宽,姜新.一种改进的Smith预估补偿方法[J].华中理工大学学报, 1999, 27(12)
[29] Dahlin E B. Designing and tuning digital controllers[J]. Instr & Contr Sys, 1968, 41 (7): 77-79
[30]朱学峰.大林算法研究[J].化工自动化仪表, 1989,14(5): 23-27
[31]郭丙君.一种预测控制算法的改进研究[J].自动化与仪器仪表, 1996, (6): 8-11
[32]张美玉等.动态矩阵控制算法在电热炉温控中的实现[J].浙江大学学报, 2002, 30(4): 360-360
[33]田社平等.人工肌肉自适应预测控制[J].仪器仪表学报, 2001, 22(3)增刊: 86-87
[34]王艳红.烟草烘丝过程的水分控制[J].北京工业大学学报, 2000, 26(4): 90-95
[35]杨马英,王树青,王骥程.大时滞过程的预测控制--催化裂化反应再生系统原料预热回路控制[J].控制理论与应用, 2000, 17(1):133-136
[36]李伟,杜玉蕾.广义预测控制在时滞系统中的应用[J].电脑应用技术, 2006, (76): 32-37
[37] Garica C E, Morari M. Internal model control: A unifying review and some new results[J]. Ind. Eng. Chem. Pro. Des. Dev., 1982,21:308-323
[38]刘川来等.一类时滞系统的模型参考自适应控制[J].仪器仪表学报, 1998, 19(4): 393-398
[39]刘希远.时变大时滞极点配置最优预报自校正PID控制器[J].控制与决策, 1990, 5(5): 19-23
[40] Huang Huixian, Wen Longyue. An Intelligent PID Control for Some Big Delay Time Plants[J].湘潭大学自然科学学报(英文).2008. 30(3):121-125
[41]王亚刚,许晓鸣.基于灵敏度的大滞后过程对象自适应PI控制器[J].控制工程, 2009, 16(3): 257-260
[42] Hou Z S, Huang W H. The model-free learning adaptive control of a nonlinear discrete-time systems[J]. Control Theory and Applications, 1998,15(6):893-899
[43]张旭红等.高层建筑风振反应的T-S模糊控制[J].系统仿真学报, 2003, 15(4): 587-589
[44]蒋健.一种大滞后过程的智能控制[J].工业控制计算机, 1995,4: 16-17, 22
[45]江智军,何小斌,杨晚辉.自来水厂混凝投药控制系统的设计及应用[J].计算机测量与控制, 2006, 14(4): 462-463, 469
[46]张旭红等.高层建筑风振反应的T-S模糊控制[J].系统仿真学报, 2003, 15(4): 587-589
[47]杨桦等.模糊控制在渠道运行系统中的应用[J].武汉大学学报(工学版), 2003, 36(1): 45-49
[48]李丽娟等.基于控制规则调整的自适应模糊控制器[J].南京化工大学学报, 2001, 23(5): 49-52
[49]周彦,何小阳,王冬丽.时变大滞后过程的专家模糊控制设计与仿真[J].系统仿真学报, 2006, 18(10): 2786-2789
[50]郭佩佩,甘艳珍,朱学峰.基于BP神经网络的水厂加药凝絮过程辨识研究[J].计算机与应用化学, 2008, 25(7): 889-892
[51]陆燕,杜继宏,李春文.延迟时间未知的时延系统神经网络补偿控制[J].清华大学学报, 1998, 38(9): 67-69
[52]马晓敏.纯滞后对象的神经网络辨识及自适应控制[J].石油大学学报(自然科学版), 1998, 22(5): 96-99
[53]王军栋,刘思远,齐维贵.混凝剂投量的RBF网络预测控制系统[J].系统仿真学报, 2009, 21(17): 5540-5543, 5548
[54]张平,苑明哲,王宏.大时滞系统自适应模糊Smith控制[J].计算机仿真, 2006, 23(9): 87-90
[55]王建辉等.一类纯滞后系统模糊Smith控制策略的控制[J].控制与决策, 1998, 13(2): 141-145
[56]杨承志,孙棣华,张长胜.系统辨识与自适应控制[M].重庆:重庆大学出版社, 2003, 7: 1-4
[57]方崇智,萧得云.过程辨识[M].北京:清华大学出版社, 1988, 8: 84
[58]潘立登,潘仰东.系统辨识与建模[M].北京:化学工业出版社, 2003, 9: 198
[59] J. Lee, W. Cho, and T.F. Edgar. An improved technique for PID controller tuning from closed loop tests[J], AIChE Journal,1990,36(12):1891-1895
[60] I.D. Landau. Identification in closed loop: a powerful design tool(better design models, simpler controllers)[J]. Control Engineering Practice, 2001, 9(1): 51-65
[61]高国燊,余文烋.自动控制原理[M].广州:华南理工大学出版社, 2003: 75-84
[62] P. Suganda, P.R. Krishnaswamy, G.P. Rangaiah. On-line process identification from closed-loop tests under PI control[J]. Transactions of the Institution of Chemical Engineers, 1998, 76(5): 451-457
[63]李超,黄德先,金以慧.有滞后环节的系统的闭环鲁棒辨识方法[J].化工自动化与仪表, 2003, 30(6): 11-15
[64]吕志民,周茂林.使用Pade近似式处理数字控制系统中的纯滞后[J].中山大学学报(自然科学版), 2001, 40(1): 114– 115
[65] M. C. Tsaia, E. J. M. Geddesb and I. Postlethwaite. Pole-zero cancellations and closed-loop properties of an H∞mixed sensitivity design problem[J]. Automatica, 1992, 28(3): 519 - 530
[66]王伟,甘艳珍,刘桂香等. MFA与PID控制器的实验比较研究[J].自动化仪表, 2008. 29(5): 27~29
[67]白桦,李圭白.混凝投药智能控制系统实现方法的探讨[J].给水排水, 2003, 29 (8): 81~83
[68]王树青等,先进控制技术及应用[M].北京:化学工业出版社, 2001, 7: 48-51
[69]侯明冬.智能内模PID控制方法研究[D].太原:太原科技大学电子信息学院, 2007
[70] Rivera D E, Morari M, S Skogestad. Internal model control: A unifying review and some new results[J]. Ind. Eng. Chem. Pro. Des. Dev.,1986.25:252~265
[71]龚剑平,王连伟,游浩.用于二阶加纯滞后过程的IMC_PID控制器的研究[J].北京化工大学学报, 1999, 26(2):59~62
[72]肖术骏等. MFA控制器在水厂加药絮凝控制系统中的应用[J].工业仪表与自动化装置, 2009, (6): 28-31
[73]温钢云,黄道平.计算机控制技术[M].广州:华南理工大学出版社, 2001, 8: 216-224
[74]周渊深.感应电动机交-交变频调速系统的内模控制技术[M].北京:电子工业出版社, 2005, 3: 75-80
[75]钟晓燕,钟聪儿,林雄等.基于遗传算法的生鲜农产品库存控制的应用[J].福建农林大学学报(自然科学版), 2009, 38(3): 301-305
[76]陈活.基于多线程模拟退火算法在配送路线选择中的应用[J].电脑知识与技术, 2009, 5(14): 3762-3763
[77]王文川,徐冬梅,邱林等.差分进化算法在水电站优化调度中的应用[J].水电能源科学, 2009, 27(3): 162-164, 192
[78]申建建,程春田,廖胜利.基于模拟退火的粒子群算法在水电站水库优化调度中的应用水力发电学报, 2009, 28(3): 10-15
[79]王凌.智能优化算法及其应用[M].北京:清华大学出版社, 2001, 10: 17,36,62
[80]莫愿斌,刘贺同,王勤.智能优化算法的综述教学研究[J].科技创新导报, 2008, (13): 2-3
[81]李雪梅,张素琴.基于仿生理论的几种优化算法综述[J].计算机应用研究, 2009, 26(6): 2032-2034
[82]王玫,朱云龙,何小贤.群体智能研究综述[J].计算机工程, 2005, 31(22): 194-196
[83]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践, 2002, 22(11): 32-38
[84] Storn,R. and Price,K., Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces[J]. Technical Report International Computer Science Institute, 1995(8):22-25
[85] Storn,R. and Price,K., Differential Evolution - A simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization,1997,11(4):341-359
[86] Storn,R. and Price,K., Minimizing the real functions of the ICEC'96 contest by Differential Evolution[C]. IEEE Int. Conf. on Evolutionary Computation, Nagoya, Japan, 1996: 842-844
[87]周艳平,顾幸生.差分进化算法研究进展[J].化工自动化及仪表, 2007, 34(3): 1-6
[88]许小健,钱德铃,张金轮.微分进化算法在暴雨强度公式参数优化中的应用[J].合肥工业大学学报, 2008, 31(4): 645-647
[89]李玉刚.基于遗传算法的PID控制系统[J].电脑知识与技术, 2009, 5(9): 2190-2192
[90]尹宏鹏,柴毅.基于蚁群算法的PID控制参数优化[J].计算机工程与应用, 2007, 43(17): 4-7
[91]段海滨,王道波,黄向华等.基于蚁群算法的PID参数优化[J].武汉大学学报(工学版), 2004, 37(5): 97-100
[92]姜立强,刘光斌,郭铮.基于差分进化算法的PID参数整定[J].计算机仿真, 2009, 26(6): 204-206
[93]唐德翠,朱雪峰.改进差分进化PID参数优化及在絮凝中的应用[J].计算机工程与应用, 2009, 45(24): 204-206
[94]肖术骏,朱学峰.一种改进的快速高效的差分进化算法[J].合肥工业大学学报(自然科学版), 2009, 32(11): 1700-1703
[95]许小健,黄小平,钱德玲.自适应加速差分进化算法[J].复杂系统与复杂性科学, 2008, 5(1): 87-92
[96] Anyong Qing. Dynamic Differential Evolution Strategy and Applications in Electromagnetic Inverse Scattering Problems[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(1): 116-125
[97] B. Yang, Z.K Zhang, Z.S Sun. Computing Nonlinearτ-Estimation Based on Dynamic Differential Evolution Strategy[J].IEEE Signal Processing Letters,2006,13(12):756-759
[98]邓泽喜,刘晓冀.差分进化算法的交叉概率因子递增策略研究[J].计算机工程与应用, 2008, 44(27): 33-36
[99]陈凯等.一种改进的集成混合遗传算法[J].统计与决策, 2008(17): 41-43
[100]袁俊刚等.差异演化算法的数值模拟研究[J].系统仿真学报, 2007, 19(20): 4646-4648
[101]白桦,李圭白.混凝投药的神经网络控制方法[J].给水排水, 2001, 27(11): 83-86
[102]王瑞红,高美凤.分层模糊控制器在水厂混凝投药过程中的用[J].计算机测量与控制, 2007, 15(1): 66~67
[103]孙达智,江智军.专家系统控制自来水生产的自动加药工艺[J].中国给水排水, 2003, 19(3): 63~65
[104]白桦,李圭白.智能控制在净水厂混凝投药过程中的应用研究[D].哈尔滨:哈尔滨工业大学博士学位论文, 2002
[105]张中炜,丁永生.净水厂混凝投药的无模型自适应控制[J].计算机仿真. 2007, 24(04): 176-178
[106]韩京清.控制理论——模型论还是控制论[J].系统科学与数学, 1989, 9(4): 328-335
[107]刘亮亮.基于无模型理论的自适应技术研究[D].哈尔滨:哈尔滨理工大学, 2007
[108]刘飞祥.无模型自适应控制理论及其应用研究[D].太原:太原科技大学, 2008
[109]邵裕森,戴先中.过程控制工程[M].北京:机械工业出版社, 2000, 5: 21-22
[2]何衍庆等.工业生产过程控制[M].北京:化学工业出版社, 2004, 2: 178
[3] Van den Hof P M J , Schrama R J P. An indirect method for transfer function estimation from closed - loop data [J] .Automatica ,1993 ,29 (6) :1523– 1527
[4]曹江涛,李平,郭丹.两阶段闭环辨识算法仿真[J].石油化工高等学校学报, 2003, 16(3): 70-74
[5]曹江涛,李平,姬晓飞.两阶段辨识算法在加热炉控制中的应用[J].工业仪表与自动化装置, 2004, (1): 14-17
[6]李超,黄德先,金以慧.有滞后环节的系统的闭环鲁棒辨识方法[J].化工自动化与仪表, 2003, 30(6): 11-15
[7]晓龙,陈政石,刘美.基于Laguerre函数预测控制模型的仿真智能控制[J].茂名学院学报, 2008, 18(6): 41-4
[8]夏伯锴,袁璞.基于Laguerre函数模型的大纯滞后系统预测控制[J].石油大学学报, 2002, 26(6): 100-112
[9]王亚刚,许晓鸣.过程对象模型在线闭环频域辨识[J].微计算机信息, 2009, 25(4-1): 282-283, 291
[10] Wang Xuelei and Shao Huihe. A phase-relevant closed-loop frequency-domain identification method and its application in optimal tuning of PI controllers[J].仪器仪表学报(英文), 2002, 23(5): 441-445
[11] LuusR, Jaakola T.H.I. Optimization by Direct Search and Systematic Reduction of the Size of Search Region[J].AIChE, 1973, 19:760-766
[12]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
[13] Yuwana, M. and Seborg, D.E. , A new method for on-line controller tuning[J]. Aiche Journal 1982, 28(3): 434-440
[14] Arthur Jutan and E. S. Rodrigues. Extension of a new method for on-line controller tuning[J]. The Canadian Journal of Chemical Engineering, Volume 62, Issue 6(802-807)
[15]刘恩国,俞金寿.单闭环测试辨识和在线PID参数整定[J].炼油化工自动化, 1992, (6): 23-29
[16] R. Manat, P.J. Fleming. Method for on-line identification of a first order plus dead-time process model[J]. ELECTRONICS LETTERS, 1995, 31(15): 1297-1298
[17]陈永云,刘爱伦,陈成良.一种PI闭环辨识新方法[J].控制工程, 2002, 9(6): 82-83, 96
[18]陈永云,刘爱伦.一种PID条件下的闭环辨识方法[J].石油化工自动化, 2004, (3): 31-32, 48
[19] Wonhui Cho, Thomas F. Edgar and Jietae Lee. Closed-loop identification of wafer temperature dynamics in a rapid thermal process[J]. Korean Journal of Chemical Engineering, 2006, 23(2): 171-175
[20] Smith O.J.M. Close control of loops with dead time[J]. Chemical Engineering Progress Trans, 1957. 53(5): 17-219
[21]克忠,郝中恕.计算机控制系统分析与设计[M].北京:清华大学出版社, 1988
[22] Hang CC. Performance study of Control Systemwith Dead Time[J]. IEEE. IECI, 1980, 27: 234-241
[23] Watanabe K. A process-model control for linear system with delay[J]. IEEE Trans AC-26, 1981, (6): 1261-1269
[24]蒋爱平,俞金寿.改进结构的Smith预估器[J].石油化工自动化. 1998, 22(3): 22-28
[25] Giles R.f,Bartley T M.Gain-adaptive dead time compensation ISA[J]. Trans, 1977, (1): 46-49
[26]王连铮,张建华,孟庆贤.增益预估自适应纯滞后补偿控制器[J].自动化学报, 1997, 23(1): 139-142
[27]李遵基,袁应钦,段志刚.主汽温自适应预估控制方法的研究[J].华北电力大学学报, 1999, 23(4)
[28]汪秉文,李国宽,姜新.一种改进的Smith预估补偿方法[J].华中理工大学学报, 1999, 27(12)
[29] Dahlin E B. Designing and tuning digital controllers[J]. Instr & Contr Sys, 1968, 41 (7): 77-79
[30]朱学峰.大林算法研究[J].化工自动化仪表, 1989,14(5): 23-27
[31]郭丙君.一种预测控制算法的改进研究[J].自动化与仪器仪表, 1996, (6): 8-11
[32]张美玉等.动态矩阵控制算法在电热炉温控中的实现[J].浙江大学学报, 2002, 30(4): 360-360
[33]田社平等.人工肌肉自适应预测控制[J].仪器仪表学报, 2001, 22(3)增刊: 86-87
[34]王艳红.烟草烘丝过程的水分控制[J].北京工业大学学报, 2000, 26(4): 90-95
[35]杨马英,王树青,王骥程.大时滞过程的预测控制--催化裂化反应再生系统原料预热回路控制[J].控制理论与应用, 2000, 17(1):133-136
[36]李伟,杜玉蕾.广义预测控制在时滞系统中的应用[J].电脑应用技术, 2006, (76): 32-37
[37] Garica C E, Morari M. Internal model control: A unifying review and some new results[J]. Ind. Eng. Chem. Pro. Des. Dev., 1982,21:308-323
[38]刘川来等.一类时滞系统的模型参考自适应控制[J].仪器仪表学报, 1998, 19(4): 393-398
[39]刘希远.时变大时滞极点配置最优预报自校正PID控制器[J].控制与决策, 1990, 5(5): 19-23
[40] Huang Huixian, Wen Longyue. An Intelligent PID Control for Some Big Delay Time Plants[J].湘潭大学自然科学学报(英文).2008. 30(3):121-125
[41]王亚刚,许晓鸣.基于灵敏度的大滞后过程对象自适应PI控制器[J].控制工程, 2009, 16(3): 257-260
[42] Hou Z S, Huang W H. The model-free learning adaptive control of a nonlinear discrete-time systems[J]. Control Theory and Applications, 1998,15(6):893-899
[43]张旭红等.高层建筑风振反应的T-S模糊控制[J].系统仿真学报, 2003, 15(4): 587-589
[44]蒋健.一种大滞后过程的智能控制[J].工业控制计算机, 1995,4: 16-17, 22
[45]江智军,何小斌,杨晚辉.自来水厂混凝投药控制系统的设计及应用[J].计算机测量与控制, 2006, 14(4): 462-463, 469
[46]张旭红等.高层建筑风振反应的T-S模糊控制[J].系统仿真学报, 2003, 15(4): 587-589
[47]杨桦等.模糊控制在渠道运行系统中的应用[J].武汉大学学报(工学版), 2003, 36(1): 45-49
[48]李丽娟等.基于控制规则调整的自适应模糊控制器[J].南京化工大学学报, 2001, 23(5): 49-52
[49]周彦,何小阳,王冬丽.时变大滞后过程的专家模糊控制设计与仿真[J].系统仿真学报, 2006, 18(10): 2786-2789
[50]郭佩佩,甘艳珍,朱学峰.基于BP神经网络的水厂加药凝絮过程辨识研究[J].计算机与应用化学, 2008, 25(7): 889-892
[51]陆燕,杜继宏,李春文.延迟时间未知的时延系统神经网络补偿控制[J].清华大学学报, 1998, 38(9): 67-69
[52]马晓敏.纯滞后对象的神经网络辨识及自适应控制[J].石油大学学报(自然科学版), 1998, 22(5): 96-99
[53]王军栋,刘思远,齐维贵.混凝剂投量的RBF网络预测控制系统[J].系统仿真学报, 2009, 21(17): 5540-5543, 5548
[54]张平,苑明哲,王宏.大时滞系统自适应模糊Smith控制[J].计算机仿真, 2006, 23(9): 87-90
[55]王建辉等.一类纯滞后系统模糊Smith控制策略的控制[J].控制与决策, 1998, 13(2): 141-145
[56]杨承志,孙棣华,张长胜.系统辨识与自适应控制[M].重庆:重庆大学出版社, 2003, 7: 1-4
[57]方崇智,萧得云.过程辨识[M].北京:清华大学出版社, 1988, 8: 84
[58]潘立登,潘仰东.系统辨识与建模[M].北京:化学工业出版社, 2003, 9: 198
[59] J. Lee, W. Cho, and T.F. Edgar. An improved technique for PID controller tuning from closed loop tests[J], AIChE Journal,1990,36(12):1891-1895
[60] I.D. Landau. Identification in closed loop: a powerful design tool(better design models, simpler controllers)[J]. Control Engineering Practice, 2001, 9(1): 51-65
[61]高国燊,余文烋.自动控制原理[M].广州:华南理工大学出版社, 2003: 75-84
[62] P. Suganda, P.R. Krishnaswamy, G.P. Rangaiah. On-line process identification from closed-loop tests under PI control[J]. Transactions of the Institution of Chemical Engineers, 1998, 76(5): 451-457
[63]李超,黄德先,金以慧.有滞后环节的系统的闭环鲁棒辨识方法[J].化工自动化与仪表, 2003, 30(6): 11-15
[64]吕志民,周茂林.使用Pade近似式处理数字控制系统中的纯滞后[J].中山大学学报(自然科学版), 2001, 40(1): 114– 115
[65] M. C. Tsaia, E. J. M. Geddesb and I. Postlethwaite. Pole-zero cancellations and closed-loop properties of an H∞mixed sensitivity design problem[J]. Automatica, 1992, 28(3): 519 - 530
[66]王伟,甘艳珍,刘桂香等. MFA与PID控制器的实验比较研究[J].自动化仪表, 2008. 29(5): 27~29
[67]白桦,李圭白.混凝投药智能控制系统实现方法的探讨[J].给水排水, 2003, 29 (8): 81~83
[68]王树青等,先进控制技术及应用[M].北京:化学工业出版社, 2001, 7: 48-51
[69]侯明冬.智能内模PID控制方法研究[D].太原:太原科技大学电子信息学院, 2007
[70] Rivera D E, Morari M, S Skogestad. Internal model control: A unifying review and some new results[J]. Ind. Eng. Chem. Pro. Des. Dev.,1986.25:252~265
[71]龚剑平,王连伟,游浩.用于二阶加纯滞后过程的IMC_PID控制器的研究[J].北京化工大学学报, 1999, 26(2):59~62
[72]肖术骏等. MFA控制器在水厂加药絮凝控制系统中的应用[J].工业仪表与自动化装置, 2009, (6): 28-31
[73]温钢云,黄道平.计算机控制技术[M].广州:华南理工大学出版社, 2001, 8: 216-224
[74]周渊深.感应电动机交-交变频调速系统的内模控制技术[M].北京:电子工业出版社, 2005, 3: 75-80
[75]钟晓燕,钟聪儿,林雄等.基于遗传算法的生鲜农产品库存控制的应用[J].福建农林大学学报(自然科学版), 2009, 38(3): 301-305
[76]陈活.基于多线程模拟退火算法在配送路线选择中的应用[J].电脑知识与技术, 2009, 5(14): 3762-3763
[77]王文川,徐冬梅,邱林等.差分进化算法在水电站优化调度中的应用[J].水电能源科学, 2009, 27(3): 162-164, 192
[78]申建建,程春田,廖胜利.基于模拟退火的粒子群算法在水电站水库优化调度中的应用水力发电学报, 2009, 28(3): 10-15
[79]王凌.智能优化算法及其应用[M].北京:清华大学出版社, 2001, 10: 17,36,62
[80]莫愿斌,刘贺同,王勤.智能优化算法的综述教学研究[J].科技创新导报, 2008, (13): 2-3
[81]李雪梅,张素琴.基于仿生理论的几种优化算法综述[J].计算机应用研究, 2009, 26(6): 2032-2034
[82]王玫,朱云龙,何小贤.群体智能研究综述[J].计算机工程, 2005, 31(22): 194-196
[83]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践, 2002, 22(11): 32-38
[84] Storn,R. and Price,K., Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces[J]. Technical Report International Computer Science Institute, 1995(8):22-25
[85] Storn,R. and Price,K., Differential Evolution - A simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization,1997,11(4):341-359
[86] Storn,R. and Price,K., Minimizing the real functions of the ICEC'96 contest by Differential Evolution[C]. IEEE Int. Conf. on Evolutionary Computation, Nagoya, Japan, 1996: 842-844
[87]周艳平,顾幸生.差分进化算法研究进展[J].化工自动化及仪表, 2007, 34(3): 1-6
[88]许小健,钱德铃,张金轮.微分进化算法在暴雨强度公式参数优化中的应用[J].合肥工业大学学报, 2008, 31(4): 645-647
[89]李玉刚.基于遗传算法的PID控制系统[J].电脑知识与技术, 2009, 5(9): 2190-2192
[90]尹宏鹏,柴毅.基于蚁群算法的PID控制参数优化[J].计算机工程与应用, 2007, 43(17): 4-7
[91]段海滨,王道波,黄向华等.基于蚁群算法的PID参数优化[J].武汉大学学报(工学版), 2004, 37(5): 97-100
[92]姜立强,刘光斌,郭铮.基于差分进化算法的PID参数整定[J].计算机仿真, 2009, 26(6): 204-206
[93]唐德翠,朱雪峰.改进差分进化PID参数优化及在絮凝中的应用[J].计算机工程与应用, 2009, 45(24): 204-206
[94]肖术骏,朱学峰.一种改进的快速高效的差分进化算法[J].合肥工业大学学报(自然科学版), 2009, 32(11): 1700-1703
[95]许小健,黄小平,钱德玲.自适应加速差分进化算法[J].复杂系统与复杂性科学, 2008, 5(1): 87-92
[96] Anyong Qing. Dynamic Differential Evolution Strategy and Applications in Electromagnetic Inverse Scattering Problems[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(1): 116-125
[97] B. Yang, Z.K Zhang, Z.S Sun. Computing Nonlinearτ-Estimation Based on Dynamic Differential Evolution Strategy[J].IEEE Signal Processing Letters,2006,13(12):756-759
[98]邓泽喜,刘晓冀.差分进化算法的交叉概率因子递增策略研究[J].计算机工程与应用, 2008, 44(27): 33-36
[99]陈凯等.一种改进的集成混合遗传算法[J].统计与决策, 2008(17): 41-43
[100]袁俊刚等.差异演化算法的数值模拟研究[J].系统仿真学报, 2007, 19(20): 4646-4648
[101]白桦,李圭白.混凝投药的神经网络控制方法[J].给水排水, 2001, 27(11): 83-86
[102]王瑞红,高美凤.分层模糊控制器在水厂混凝投药过程中的用[J].计算机测量与控制, 2007, 15(1): 66~67
[103]孙达智,江智军.专家系统控制自来水生产的自动加药工艺[J].中国给水排水, 2003, 19(3): 63~65
[104]白桦,李圭白.智能控制在净水厂混凝投药过程中的应用研究[D].哈尔滨:哈尔滨工业大学博士学位论文, 2002
[105]张中炜,丁永生.净水厂混凝投药的无模型自适应控制[J].计算机仿真. 2007, 24(04): 176-178
[106]韩京清.控制理论——模型论还是控制论[J].系统科学与数学, 1989, 9(4): 328-335
[107]刘亮亮.基于无模型理论的自适应技术研究[D].哈尔滨:哈尔滨理工大学, 2007
[108]刘飞祥.无模型自适应控制理论及其应用研究[D].太原:太原科技大学, 2008
[109]邵裕森,戴先中.过程控制工程[M].北京:机械工业出版社, 2000, 5: 21-22