多变量智能预测控制方法及其应用研究
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摘要
电站的主要动态特性包括非线性、多变量耦合和不确定性,采用传统控制方法难以实施有效控制。本文针对联合循环燃气轮机的特点,采用小偏差线性化法建模燃气轮机不同工况的模型,将多变量广义预测控制策略应用于燃气轮机系统转速和功率的控制。研究了基于线性模型的监督预测控制算法,并将其推广到多变量系统,针对单元机组协调控制进行仿真。提出了基于模糊神经网络模型的非线性监督预测控制优化算法,同时通过引入Kuhn-Tucker条件可以处理受限问题。文中以燃气轮机为例,在不同工况下进行了仿真研究,与线性模型督预测控制效果进行比较,仿真结果表明了其良好的控制效果和跟踪能力。
The major dynamics of the power plant include nonlinearities behavior, coupling effect, and uncertainties. Traditional control strategy could not offer satisfactory result. Using the linearization modeling technique, this paper deals with the velocity and power control of gas turbine in combined cycle power plant(CCPP) by multivariable generalized predictive control method. Then, a multivariable supervisory predictive control is proposed, which has been simulated in thermal power plant coordinated system. Third, this paper proposes a nonlinear multivariable supervisory predictive control. Neuro-fuzzy model is incorporated to represent the plant nonlinearity. In this way, constraints can still be tackled using Kuhn-Tucke condition. Gas turbine control is presented to illustrate the implementation and the performance of the proposed method. Comparative control studies suggest an improvement over conventional controller.
The major dynamics of the power plant include nonlinearities behavior, coupling effect, and uncertainties. Traditional control strategy could not offer satisfactory result. Using the linearization modeling technique, this paper deals with the velocity and power control of gas turbine in combined cycle power plant(CCPP) by multivariable generalized predictive control method. Then, a multivariable supervisory predictive control is proposed, which has been simulated in thermal power plant coordinated system. Third, this paper proposes a nonlinear multivariable supervisory predictive control. Neuro-fuzzy model is incorporated to represent the plant nonlinearity. In this way, constraints can still be tackled using Kuhn-Tucke condition. Gas turbine control is presented to illustrate the implementation and the performance of the proposed method. Comparative control studies suggest an improvement over conventional controller.
引文
[1]Q in S J , Badgwell T A. An overview of industrial model predictive control technology[A].Chemical Process Control-V[C].CACHE, AIChE,1997.232-256.
[2]Mayne D Q.Nonlinear model predictive control: An assessment[A]. Chemical Process Control-V[C].CACHE, AIChE,1997.217-231.
[3]Chen H, Allgower F.Nonlinearmodel predictive control schemes with guaranteed stability.Nonlinear Model Based Process Control[M].Kluwer Academic Publishers,Netherlands:1998.465-494.
[4]MorariM , Lee J H. Model predictive control: The good,the bad and the ugly[A].Chemical Process Control-IV[C].CACHE,AIChE,1991.419-444.
[5]Ricker N L. Model predictive control: State of the art[A]. Chemical Process Control-IV[C]. CACHE,AIChE,1991.271-296.
[6]Lee J H. Recent advances in model predictive control and other related areas[A]. Chemical Process Control-V[C]. CACHE, AIChE,1997. 201-216.
[7]席裕庚.关于预测控制的进一步思考[J].控制理论与应用, 1994,11(2): 219-221.
[8]徐立鸿.预测控制的研究现状及问题[J].控制理论与应用, 1994, 11(1): 121-125.
[9]Maner B R, Dolyle F J. Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order volterra models[J]. Automatica,1996,32(9):1285-1301.
[10]Genceli H, Nikolaon M. Design of Robust Constrained Model Predictive Controllers with Volterra Series[J]. AIchE J,1995, 41(9): 2098-2107.
[11]Doyle F J, Ogannaike B A, Pearson R U. Nonlinear Model-Based Control Using Second-Order Volterra Models[J]. Automatica,1995,31(5): 697-714.
[12]Maner B R, Doyle F J, Ogunnaiue B A. Nonlinear Model Predictive Control of a Simulated Multivariable Polymerization Reactor Using Second-Order Volterra Models [J]. Automatica,1996,32(9):1285-1301.
[13]刘强,许晓鸣,张卫东.基于Volterra模型的非线性系统预测控制[J].控制与决策,2000,15(3):339-344.
[14]Wang W. Generalized Predictive Control of Nonlinear Systems of the Hammerstein Forms [J]. Control Theory and Applications,1994,11(6): 672-680.
[15]Fruzzetti K P, Palazoglu A, Mcdonald K A. Nonlinear Model Predictive Control Using Hammerstein Models [J]. Journal of Process Control,1997,7(1): 31-41.
[16]Pomerleau D A, Hodouin D. A procedure for the design and evaluation of decentralized and model-base predictive multivariable controllers for a pellet cooling process [J]. Computers and Chemical Engineering, 2003,27(2):217-233.
[17]张泉灵,王树青.基于Hammerstein模型的非线性预测控制[J].浙江大学学报,2002,36(2):119-122.
[18]Norguary S L, Palazoglu A, Romagnoli J A. Nonlinear Model Predictive Control of PH Neutralization Using Wiener Models [C].Proc 13th IFAC World Cong [C]. San Fransisco,1996:31-36.
[19]Rohit S, Patwardham S, Laksh M. Constrained Nonlinear MPC Using Hammerstein and Wiener Models:PLS Framework [J]. AIchE J, 1998,44(7): 1611-1622.
[20]王慧燕,诸静.基于EGA的非线性预测控制器[J].控制与决策,2002, 17(2): 252-254.
[21]Roubos J A F, Mollov S, Babusks B. Fuzzy Model-Based Predictive Control Using Takogi-Sngeno Models [C]. Int J Approx Reas, 1999,22: 3-30.
[22]Wong C H, Shah S L. Adaptive fuzzy relational predictive control [J].Fuzzy Sets and Systems,2000,115(2):247-260.
[23]Zhao F, Qu J. Pattern-base fuzzy predictive control for a chemical process with deed time [J]. Engineering Applications of Artificial Intelligence,2000,13(1):37-45.
[24]Tian Y, Zhang J. Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models[J]. Chemical Engineering and Processing,2002,41(7):531-538.
[25]Benne M, Grondin P B. Artificial neural network for modeling and predictive control of an industrial evaporation process [J]. Journal of Food Engineering,2000,46(4):227-234.
[26]Gu DB, Hu H H. Neural predictive control for a car-like mobile robot [J]. Robotics and Autonomous System,2002,39(2):73-86.
[27]Jonas S, Mukul A. Trajectory tracking in batch processes using neural controllers [J]. Journal of Food Engineering,2002,15(3): 41-51.
[28]Tsai P F, Chu J Z. Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural network [J]. Journal of Process Control,2003,13(5):423-435.
[29]Jose M N, Dolors G. Parameter estimation with genetic algorithm incontrol of fed-batch reactors [J]. Chemical Engineering and Processing,2002, 41(5):303-309.
[30]Grosman B, Lewin D R. Automated nonlinear model predictive control using genetic programming [J]. Computers and Chemical Engineering, 2002,26(5): 631-640.
[31] Hong Yoon-Seok, Bhamidiman R. Evolutionary self-organising modeling of a municipal wastewater treatmeng plant [J]. Water Research 2003, 37(6): 1199-1212.
[32]de Prada,C.,Valentin,A.(1996).Set-point optimization in multivariable constrainted predictive control. Proceedings of the 13th World Congress of IFAC International Federation of Automatic Control,San Francisco,June 30-July 5,pp.351-425.
[33]Katebi,M.,Johnson,M.(1997).Predictive control design for large-scale systems. Automatica,Vol.33,no.3,PP.421-425.
[34]Ramirez,C.,Ruiz,M.,Lago, C. Ruiz, C.,Santollani, O.,Johston, Ch.(1995). Controladores por matriz dinamica a precesos de re finacion. Proceeding of the International Society for Measurement and Control, ISA Show Brasil.
[35]Tadeo,F.,Alvarez,T.,Grimble,M.,Vilanova,R.(2002). Implementation of constrained predictive outer-loop controllers:Application to a boiler control system. Proceedings of the IFAC 15th Triennial World Congress, Barcelona,Spain.
[36]刘向杰.联合循环电厂余热锅炉的监督预测控制策略[J].中国电机工程学报, 2007,27(2):52- 57.
[37]A. Bemporad, A. Casavola, and E. Mosca,”Nonliner control of constrained liner systems via predictive reference management,”IEEE Trans. Autom. Control,vol.42,no.3,pp.340-349,Mar.1997.
[38]D.Angeli and E.Mosca,”Command governors for constrained nonlinear systems,”IEEE Trans. Autom.Control,vol.44,no.4,pp.816-820,Apr.1999.
[39]E.Gilbert and I.Kolmanovsky,”Fast reference governors for systems with state and control constraints and disturbance inputs,”Int.J.Robust Nonlinear Control,vol.9,no.15pp.1117-1141, 1999.
[40]Saez D,Cipriano A,Ordys A W.Optimisation of industrial processes at supervisory level[M].Great Britain:Spinger-verlag London Limited, 2002.
[41]Doris Saez D , Freddy Milla and Andrzej Ordys.hybrid predictive supervisory control based on genetic algorithms for a gas turbine of combinedcycle power plants. Proceeding of the European Control Conference 2007. Kos,Greece, July 2-5,2007.
[42]雎刚,徐治皋.一种基于变结构的预测控制算法及其应用研究[J].中国电机工程学报,2001,21(7):111-114.
[43]窦春霞.基于混沌理论的过热汽温最优预测控制策略[J].燃烧科学与技术,2003,9(4):367-371.190-194.
[44]雎刚,韦红旗,陈绍炳等.单元机组负荷多变量模型预测控制[J].中国电机工程学报,2002,22(4): 144-148.
[45]李俊,孙德敏,魏衡华,等. PID自动整定和阶梯式GPC在大型电站锅炉中的应用.电力系统自动化,2000,24(18):45-48.
[46]杨平,翁思义.连续时间广义预测控制用于电厂锅炉汽压控制的仿真研究.中国电机工程学报,1993,13(1):50-55.
[47]Sanchez L,Arroyo F,Villavicencio R. Steam temperature fuzzy controllers in fossil power plant. Proceedigs of the IFAC Conference on Control of Power Plants and Power Systems, SINPOWER’95, December 6-8 Cancun, pp.61～66.
[48]Zhiteckij L , Skurikhin V, Procenko N,etc.(1995). Design of an adaptive robust and careful controller for a thermal power plant. Proceedings of the IFAC Conference on Control of Power Plants and Power Systems, SIPOWER’95, December 6-8,Cancun,pp.221～226.
[49]Clarke D W , Moh tadi C, Tuffs P S. Generalized P redictive Contro l: Part 1 and Part 2. A utomatica, 1987, 23 (1) : 137～160
[50]李辛毅,安宁等.复杂连接形式的汽轮机长叶片振动特性研究[J].动力工程,1997,17(1): 38-40.
[51]潘蕾.重型单轴燃气轮-发电机系统的变工况建模研究及数字仿真[D].南京:东南大学动力工程系,2002
[52]Ellis R,Li X,Riggs J.Modeling and optimization of a model IV fluidized catalytic cracking unit[J].AIChE Journal,1998,44(9):2068-2079.
[53] Munoz C,Cipriano A.An integrated system for supervision and economic optimal control of mineral processing plants[J].Minerals Engineering,1999,12(6):627-643.
[54]边立秀,周俊霞.热工控制系统.中国电力出版社,2002.
[55]张建华,董菲,侯国莲.基于神经网络预测控制的单元机组协调控制策略研究[J].动力工程.2006,26(3):392-395.
[56]Miguel A. B. et.al., Predictive control based on neural network models with I/O linearization. International Journal of Control. Vol.72, no.17,1999, 1538-1554.
[57]Zheng, A.: Computationally efficient nonlinear model predictive control algorithm for control of constrained nonlinear system. In“Nonlinear predictive control”edited by Basil Kouvaritakis. United Kingdom, 2003. Pp173-187.
[58]H. A.B.te Braake, et.al., Linear predictive control based on approximate input-output feedback linearization. IEE Proc.-Control Theory and Application. Vol.146, no.4, 1999, 295-300.
[59]李少远,席裕庚.模糊动态环境下复杂系统得满意优化控制.自动化学报.2002, 28(3): 408-412.
[60]刘向杰,周孝信,柴天佑.模糊控制研究的现状与新发展.(综述)信息与控制. 1999,28(4): 283-292.
[61]Xiang-Jie Liu, Felipe Lara-Rosano and C.W. Chan. Model-reference Adaptive Control Using Neuro-fuzzy Network. IEEE Trans. Systems, Man, and Cybernetics, Part C.(Regular Paper) Vol.34, No.3, pp.302-309. August, 2004.
[62]Xiang-Jie Liu, Felipe Lara-Rosano and C.W. Chan. Adaptive Integrating Control based on Neuro-fuzzy Network. Journal of Control and Intelligent Systems, 31(3): 173-180, 2003.
[63]J. Zhang and A.J. Morris,“Long range predictive control of nonlinear processes based on recurrent neuro-fuzzy network models,”Neural Comput & Application, pp. 50-59, 2000.
[64]M. Brown and C. J. Harris, Neurofuzzy Adaptive Modelling and Control. Englewood Cliffs, NJ: Prentice-Hall, 1994.
[65]Luenberger,D.(1989).Programacion lineal y no lineal. Addison Wesley Iberoamericana, Deleware, United States of America.
[2]Mayne D Q.Nonlinear model predictive control: An assessment[A]. Chemical Process Control-V[C].CACHE, AIChE,1997.217-231.
[3]Chen H, Allgower F.Nonlinearmodel predictive control schemes with guaranteed stability.Nonlinear Model Based Process Control[M].Kluwer Academic Publishers,Netherlands:1998.465-494.
[4]MorariM , Lee J H. Model predictive control: The good,the bad and the ugly[A].Chemical Process Control-IV[C].CACHE,AIChE,1991.419-444.
[5]Ricker N L. Model predictive control: State of the art[A]. Chemical Process Control-IV[C]. CACHE,AIChE,1991.271-296.
[6]Lee J H. Recent advances in model predictive control and other related areas[A]. Chemical Process Control-V[C]. CACHE, AIChE,1997. 201-216.
[7]席裕庚.关于预测控制的进一步思考[J].控制理论与应用, 1994,11(2): 219-221.
[8]徐立鸿.预测控制的研究现状及问题[J].控制理论与应用, 1994, 11(1): 121-125.
[9]Maner B R, Dolyle F J. Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order volterra models[J]. Automatica,1996,32(9):1285-1301.
[10]Genceli H, Nikolaon M. Design of Robust Constrained Model Predictive Controllers with Volterra Series[J]. AIchE J,1995, 41(9): 2098-2107.
[11]Doyle F J, Ogannaike B A, Pearson R U. Nonlinear Model-Based Control Using Second-Order Volterra Models[J]. Automatica,1995,31(5): 697-714.
[12]Maner B R, Doyle F J, Ogunnaiue B A. Nonlinear Model Predictive Control of a Simulated Multivariable Polymerization Reactor Using Second-Order Volterra Models [J]. Automatica,1996,32(9):1285-1301.
[13]刘强,许晓鸣,张卫东.基于Volterra模型的非线性系统预测控制[J].控制与决策,2000,15(3):339-344.
[14]Wang W. Generalized Predictive Control of Nonlinear Systems of the Hammerstein Forms [J]. Control Theory and Applications,1994,11(6): 672-680.
[15]Fruzzetti K P, Palazoglu A, Mcdonald K A. Nonlinear Model Predictive Control Using Hammerstein Models [J]. Journal of Process Control,1997,7(1): 31-41.
[16]Pomerleau D A, Hodouin D. A procedure for the design and evaluation of decentralized and model-base predictive multivariable controllers for a pellet cooling process [J]. Computers and Chemical Engineering, 2003,27(2):217-233.
[17]张泉灵,王树青.基于Hammerstein模型的非线性预测控制[J].浙江大学学报,2002,36(2):119-122.
[18]Norguary S L, Palazoglu A, Romagnoli J A. Nonlinear Model Predictive Control of PH Neutralization Using Wiener Models [C].Proc 13th IFAC World Cong [C]. San Fransisco,1996:31-36.
[19]Rohit S, Patwardham S, Laksh M. Constrained Nonlinear MPC Using Hammerstein and Wiener Models:PLS Framework [J]. AIchE J, 1998,44(7): 1611-1622.
[20]王慧燕,诸静.基于EGA的非线性预测控制器[J].控制与决策,2002, 17(2): 252-254.
[21]Roubos J A F, Mollov S, Babusks B. Fuzzy Model-Based Predictive Control Using Takogi-Sngeno Models [C]. Int J Approx Reas, 1999,22: 3-30.
[22]Wong C H, Shah S L. Adaptive fuzzy relational predictive control [J].Fuzzy Sets and Systems,2000,115(2):247-260.
[23]Zhao F, Qu J. Pattern-base fuzzy predictive control for a chemical process with deed time [J]. Engineering Applications of Artificial Intelligence,2000,13(1):37-45.
[24]Tian Y, Zhang J. Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models[J]. Chemical Engineering and Processing,2002,41(7):531-538.
[25]Benne M, Grondin P B. Artificial neural network for modeling and predictive control of an industrial evaporation process [J]. Journal of Food Engineering,2000,46(4):227-234.
[26]Gu DB, Hu H H. Neural predictive control for a car-like mobile robot [J]. Robotics and Autonomous System,2002,39(2):73-86.
[27]Jonas S, Mukul A. Trajectory tracking in batch processes using neural controllers [J]. Journal of Food Engineering,2002,15(3): 41-51.
[28]Tsai P F, Chu J Z. Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural network [J]. Journal of Process Control,2003,13(5):423-435.
[29]Jose M N, Dolors G. Parameter estimation with genetic algorithm incontrol of fed-batch reactors [J]. Chemical Engineering and Processing,2002, 41(5):303-309.
[30]Grosman B, Lewin D R. Automated nonlinear model predictive control using genetic programming [J]. Computers and Chemical Engineering, 2002,26(5): 631-640.
[31] Hong Yoon-Seok, Bhamidiman R. Evolutionary self-organising modeling of a municipal wastewater treatmeng plant [J]. Water Research 2003, 37(6): 1199-1212.
[32]de Prada,C.,Valentin,A.(1996).Set-point optimization in multivariable constrainted predictive control. Proceedings of the 13th World Congress of IFAC International Federation of Automatic Control,San Francisco,June 30-July 5,pp.351-425.
[33]Katebi,M.,Johnson,M.(1997).Predictive control design for large-scale systems. Automatica,Vol.33,no.3,PP.421-425.
[34]Ramirez,C.,Ruiz,M.,Lago, C. Ruiz, C.,Santollani, O.,Johston, Ch.(1995). Controladores por matriz dinamica a precesos de re finacion. Proceeding of the International Society for Measurement and Control, ISA Show Brasil.
[35]Tadeo,F.,Alvarez,T.,Grimble,M.,Vilanova,R.(2002). Implementation of constrained predictive outer-loop controllers:Application to a boiler control system. Proceedings of the IFAC 15th Triennial World Congress, Barcelona,Spain.
[36]刘向杰.联合循环电厂余热锅炉的监督预测控制策略[J].中国电机工程学报, 2007,27(2):52- 57.
[37]A. Bemporad, A. Casavola, and E. Mosca,”Nonliner control of constrained liner systems via predictive reference management,”IEEE Trans. Autom. Control,vol.42,no.3,pp.340-349,Mar.1997.
[38]D.Angeli and E.Mosca,”Command governors for constrained nonlinear systems,”IEEE Trans. Autom.Control,vol.44,no.4,pp.816-820,Apr.1999.
[39]E.Gilbert and I.Kolmanovsky,”Fast reference governors for systems with state and control constraints and disturbance inputs,”Int.J.Robust Nonlinear Control,vol.9,no.15pp.1117-1141, 1999.
[40]Saez D,Cipriano A,Ordys A W.Optimisation of industrial processes at supervisory level[M].Great Britain:Spinger-verlag London Limited, 2002.
[41]Doris Saez D , Freddy Milla and Andrzej Ordys.hybrid predictive supervisory control based on genetic algorithms for a gas turbine of combinedcycle power plants. Proceeding of the European Control Conference 2007. Kos,Greece, July 2-5,2007.
[42]雎刚,徐治皋.一种基于变结构的预测控制算法及其应用研究[J].中国电机工程学报,2001,21(7):111-114.
[43]窦春霞.基于混沌理论的过热汽温最优预测控制策略[J].燃烧科学与技术,2003,9(4):367-371.190-194.
[44]雎刚,韦红旗,陈绍炳等.单元机组负荷多变量模型预测控制[J].中国电机工程学报,2002,22(4): 144-148.
[45]李俊,孙德敏,魏衡华,等. PID自动整定和阶梯式GPC在大型电站锅炉中的应用.电力系统自动化,2000,24(18):45-48.
[46]杨平,翁思义.连续时间广义预测控制用于电厂锅炉汽压控制的仿真研究.中国电机工程学报,1993,13(1):50-55.
[47]Sanchez L,Arroyo F,Villavicencio R. Steam temperature fuzzy controllers in fossil power plant. Proceedigs of the IFAC Conference on Control of Power Plants and Power Systems, SINPOWER’95, December 6-8 Cancun, pp.61～66.
[48]Zhiteckij L , Skurikhin V, Procenko N,etc.(1995). Design of an adaptive robust and careful controller for a thermal power plant. Proceedings of the IFAC Conference on Control of Power Plants and Power Systems, SIPOWER’95, December 6-8,Cancun,pp.221～226.
[49]Clarke D W , Moh tadi C, Tuffs P S. Generalized P redictive Contro l: Part 1 and Part 2. A utomatica, 1987, 23 (1) : 137～160
[50]李辛毅,安宁等.复杂连接形式的汽轮机长叶片振动特性研究[J].动力工程,1997,17(1): 38-40.
[51]潘蕾.重型单轴燃气轮-发电机系统的变工况建模研究及数字仿真[D].南京:东南大学动力工程系,2002
[52]Ellis R,Li X,Riggs J.Modeling and optimization of a model IV fluidized catalytic cracking unit[J].AIChE Journal,1998,44(9):2068-2079.
[53] Munoz C,Cipriano A.An integrated system for supervision and economic optimal control of mineral processing plants[J].Minerals Engineering,1999,12(6):627-643.
[54]边立秀,周俊霞.热工控制系统.中国电力出版社,2002.
[55]张建华,董菲,侯国莲.基于神经网络预测控制的单元机组协调控制策略研究[J].动力工程.2006,26(3):392-395.
[56]Miguel A. B. et.al., Predictive control based on neural network models with I/O linearization. International Journal of Control. Vol.72, no.17,1999, 1538-1554.
[57]Zheng, A.: Computationally efficient nonlinear model predictive control algorithm for control of constrained nonlinear system. In“Nonlinear predictive control”edited by Basil Kouvaritakis. United Kingdom, 2003. Pp173-187.
[58]H. A.B.te Braake, et.al., Linear predictive control based on approximate input-output feedback linearization. IEE Proc.-Control Theory and Application. Vol.146, no.4, 1999, 295-300.
[59]李少远,席裕庚.模糊动态环境下复杂系统得满意优化控制.自动化学报.2002, 28(3): 408-412.
[60]刘向杰,周孝信,柴天佑.模糊控制研究的现状与新发展.(综述)信息与控制. 1999,28(4): 283-292.
[61]Xiang-Jie Liu, Felipe Lara-Rosano and C.W. Chan. Model-reference Adaptive Control Using Neuro-fuzzy Network. IEEE Trans. Systems, Man, and Cybernetics, Part C.(Regular Paper) Vol.34, No.3, pp.302-309. August, 2004.
[62]Xiang-Jie Liu, Felipe Lara-Rosano and C.W. Chan. Adaptive Integrating Control based on Neuro-fuzzy Network. Journal of Control and Intelligent Systems, 31(3): 173-180, 2003.
[63]J. Zhang and A.J. Morris,“Long range predictive control of nonlinear processes based on recurrent neuro-fuzzy network models,”Neural Comput & Application, pp. 50-59, 2000.
[64]M. Brown and C. J. Harris, Neurofuzzy Adaptive Modelling and Control. Englewood Cliffs, NJ: Prentice-Hall, 1994.
[65]Luenberger,D.(1989).Programacion lineal y no lineal. Addison Wesley Iberoamericana, Deleware, United States of America.