广义预测控制算法简化实现方法研究
|
摘要
生产实践发展的需要催生了模型预测控制方法,模型预测控制理论发展的目的是更好地服务于生产实践。因此,如何把先进的模型预测控制算法应用于实际控制中是模型预测控制发展的根本性问题之一。论文正是从这一观念出发,在前人研究的基础上,对广义预测控制算法提出了一系列的简化实现方法。论文包括以下内容:
首先,介绍了模型预测控制方法的发展过程、现状、目前存在的局限性以及发展趋势。分析了广义预测控制算法的特点及应用时存在的困难。在总结模型预测控制方法常用简化实现方法和广义预测控制算法简化实现已取得的成果的基础上,提出论文所要进行的研究工作。
第二,对于物理可实现的多变量系统,其CARIMA模型的A(z~(-1))与C(z~(-1))多项式矩阵总可以构造成对角形式,从而可以给出这种模型的广义预测控制算法的完整求解过程。在算法的推导过程中,显式地考虑了过程纯滞后项,以提高计算效率。利用这种形式的模型结构,不但广义预测控制算法的求解过程可以得到很大程度的简化,而且相应的模型参数辨识问题也得到了简化,可以把一个多输入多输出模型的大型参数辨识问题分解成多个多输入单输出模型的小型参数辨识问题。
第三,通过对模型预测输出自由响应项的进一步分析,得到了状态反馈结构形式的广义预测控制器,控制增量等于控制器系数与设定值、过程输入输出历史数据的乘积,控制器系数只与模型参数和设计参数有关,控制器系数维数由预测时域与模型结构参数决定。消除了在非自适应模式下在线求解模型输出自由响应的必要,可以像PID控制器一样实现广义预测控制器。
第四,利用CARIMA模型直接递推得到了更加简洁的广义预测控制器,控制增量等于控制器系数与设定值、过程输入输出历史数据、模型预测误差历史数据的乘积,控制器系数只与模型参数和设计参数有关,控制器系数维数只由模型结构参数决定。在自适应模式下无需进行Diophantine方程的求解,在非自适应模式下将实现难度与计算量降到了最低。
第五,通过对多变量广义预测控制算法内在机理的分析,指出多变量广义预测控制算法实际上是一种函数映射,是从模型参数空间到控制器系数空间的映射,并利用神经网络的映射能力来实现多变量广义预测控制器系数的快速计算,
It is the development of industrial process control that has speeded up the emerging of model predictive control (MPC) methodology and the basic motivation of using MPC technology is to ensure significant economic benefits. Thus, one of the most essential problems of MPC technology development is how to implement these advanced algorithms in real world effectively. Just from this view, a set of simplified implementation for generalized predictive control (GPC) are presented based on the current framework of MPC. The dissertation is organized as follows:First, a brief history of MPC technology development is presented, followed by its current features and limitations of existing technology and its future trends. The distinguishing features of GPC approach and its application difficulty are discussed. After summarizing the general ways to simplify the implementation of MPC and the achievements in simplifying implementation for GPC, the main research works are given.Second, for most of the physical realizable processes, the matrices C(z-1) andA(z-1) of their Controlled Autoregressive Integrated Moving Average (CARIMA)model can always be diagonally constructed, so that the formulation of GPC can be developed in more detail while explicitly considering the dead time in order to improve the computational efficiency. This model structure greatly simplifies not only the development of the GPC but also its parameter identification which can be transformed into a set of multiple input single output model parameter identification problems.Third, a state-feedback like controller of GPC is obtained by further manipulating the free response of the output predictor, whose control increment equals to the controller's coefficients multiplied by set-points and historical plant input and output data. The controller's coefficients are only determined by the model parameters and design parameters and its dimension is determined by the model structure parameters and predictive horizon, which eliminates the need to compute the free response on-line and makes the implementation of GPC controller as easy as that
of PID under the non-adaptive mode.Fourth, a more concise GPC controller is obtained by directly manipulating the output predictor using the multivariable CARIMA model recursively, whose control moves are the product of the controller's coefficients and set-points, historical input/output data of the plant and predictive errors of the predictor. The controller's coefficients are determined only by the model parameters and design parameters and its dimension only depends on the orders of the model, which avoids solving Diophantine equations on-line under adaptive mode and reduces difficulties of implementing the GPC controller and the computational overhead to the lowest limit under the non-adaptive mode.Fifth, it is pointed out that the multivariable GPC algorithm is essentially a kind of functional mapping from the multivariable process model parameters' space to the multivariable GPC controller's coefficients' space by analyzing its intrinsic mechanism. This mapping can be realized by BP neural network to obtain the GPC controller's coefficients from the model parameters directly, which can extremely reduce the computational overhead on-line and simplify the implementation of the GPC controller.Sixth, the above schemes developed in this dissertation are compared by a set of contrast experiments on a nonlinear liquid level equipment. Their feasibility, validity and equivalency are demonstrated by experiment results.Last, a summary is given to show what has been done in this paper. The applicable scope of these methods developed in this dissertation is discussed, followed by some items that must pay attention to when implement these methods and future potential research opportunities.
首先,介绍了模型预测控制方法的发展过程、现状、目前存在的局限性以及发展趋势。分析了广义预测控制算法的特点及应用时存在的困难。在总结模型预测控制方法常用简化实现方法和广义预测控制算法简化实现已取得的成果的基础上,提出论文所要进行的研究工作。
第二,对于物理可实现的多变量系统,其CARIMA模型的A(z~(-1))与C(z~(-1))多项式矩阵总可以构造成对角形式,从而可以给出这种模型的广义预测控制算法的完整求解过程。在算法的推导过程中,显式地考虑了过程纯滞后项,以提高计算效率。利用这种形式的模型结构,不但广义预测控制算法的求解过程可以得到很大程度的简化,而且相应的模型参数辨识问题也得到了简化,可以把一个多输入多输出模型的大型参数辨识问题分解成多个多输入单输出模型的小型参数辨识问题。
第三,通过对模型预测输出自由响应项的进一步分析,得到了状态反馈结构形式的广义预测控制器,控制增量等于控制器系数与设定值、过程输入输出历史数据的乘积,控制器系数只与模型参数和设计参数有关,控制器系数维数由预测时域与模型结构参数决定。消除了在非自适应模式下在线求解模型输出自由响应的必要,可以像PID控制器一样实现广义预测控制器。
第四,利用CARIMA模型直接递推得到了更加简洁的广义预测控制器,控制增量等于控制器系数与设定值、过程输入输出历史数据、模型预测误差历史数据的乘积,控制器系数只与模型参数和设计参数有关,控制器系数维数只由模型结构参数决定。在自适应模式下无需进行Diophantine方程的求解,在非自适应模式下将实现难度与计算量降到了最低。
第五,通过对多变量广义预测控制算法内在机理的分析,指出多变量广义预测控制算法实际上是一种函数映射,是从模型参数空间到控制器系数空间的映射,并利用神经网络的映射能力来实现多变量广义预测控制器系数的快速计算,
It is the development of industrial process control that has speeded up the emerging of model predictive control (MPC) methodology and the basic motivation of using MPC technology is to ensure significant economic benefits. Thus, one of the most essential problems of MPC technology development is how to implement these advanced algorithms in real world effectively. Just from this view, a set of simplified implementation for generalized predictive control (GPC) are presented based on the current framework of MPC. The dissertation is organized as follows:First, a brief history of MPC technology development is presented, followed by its current features and limitations of existing technology and its future trends. The distinguishing features of GPC approach and its application difficulty are discussed. After summarizing the general ways to simplify the implementation of MPC and the achievements in simplifying implementation for GPC, the main research works are given.Second, for most of the physical realizable processes, the matrices C(z-1) andA(z-1) of their Controlled Autoregressive Integrated Moving Average (CARIMA)model can always be diagonally constructed, so that the formulation of GPC can be developed in more detail while explicitly considering the dead time in order to improve the computational efficiency. This model structure greatly simplifies not only the development of the GPC but also its parameter identification which can be transformed into a set of multiple input single output model parameter identification problems.Third, a state-feedback like controller of GPC is obtained by further manipulating the free response of the output predictor, whose control increment equals to the controller's coefficients multiplied by set-points and historical plant input and output data. The controller's coefficients are only determined by the model parameters and design parameters and its dimension is determined by the model structure parameters and predictive horizon, which eliminates the need to compute the free response on-line and makes the implementation of GPC controller as easy as that
of PID under the non-adaptive mode.Fourth, a more concise GPC controller is obtained by directly manipulating the output predictor using the multivariable CARIMA model recursively, whose control moves are the product of the controller's coefficients and set-points, historical input/output data of the plant and predictive errors of the predictor. The controller's coefficients are determined only by the model parameters and design parameters and its dimension only depends on the orders of the model, which avoids solving Diophantine equations on-line under adaptive mode and reduces difficulties of implementing the GPC controller and the computational overhead to the lowest limit under the non-adaptive mode.Fifth, it is pointed out that the multivariable GPC algorithm is essentially a kind of functional mapping from the multivariable process model parameters' space to the multivariable GPC controller's coefficients' space by analyzing its intrinsic mechanism. This mapping can be realized by BP neural network to obtain the GPC controller's coefficients from the model parameters directly, which can extremely reduce the computational overhead on-line and simplify the implementation of the GPC controller.Sixth, the above schemes developed in this dissertation are compared by a set of contrast experiments on a nonlinear liquid level equipment. Their feasibility, validity and equivalency are demonstrated by experiment results.Last, a summary is given to show what has been done in this paper. The applicable scope of these methods developed in this dissertation is discussed, followed by some items that must pay attention to when implement these methods and future potential research opportunities.
引文
[1] 席裕庚,许晓鸣.预测控制的现状、机理及发展前景.孙优贤,钱积新编,工业过程模型化及控制.杭州:浙江大学出版社,1988,1-6.
[2] Garcia, C. E., Prett, D. M., Morari, M. Model predictive control: Theory and practice—a survey. Automatica, 1989, 25(3):335-348.
[3] 席裕庚,许晓鸣,张钟俊.预测控制的研究现状和多层智能预测控制.控制理论与应用,1989,6(2):1-7.
[4] Richer, N. L.. Model predictive control: State of the art. In Y. Arkun, W. H. Ray (Eds.), Chemical process control—CPC Ⅳ, Fourth international conference on chemical process control (pp. 271-296). Amsterdam:Elsevier, 1991.
[5] Morari, M., Lee, J. H.. Model predictive control: The good, the bad, and the ugly. In Y. Arkun, W. H. Ray (Eds.), Chemical process control—CPC Ⅳ, Fourth international conference on chemical process control (pp. 419-444). Amsterdam:Elsevier, 1991.
[6] Muske, K. R. Rawlings, J. B.. Model predictive control with linear models. A.I. CH.E Jouranl, 1993, 39(2):262-287.
[7] Froisy, J. B.. Model predictive control: Past, present and future. ISA Transactions, 1994, 33:235-243.
[8] Hillestad, M., Andersen, K. S.. Model predictive control for grade transitions of a polypropylene reactor. In Proceedings of the 4th European symposium on computer aided process engineering (ESCAPE 4), Dublin, March 1994.
[9] Rawlings, J. B., Meadows, E. S., Muske, K.. Nonlinear model predictive control: a tutorial and survey. In Proceedings of IFAC ADCHEM, Japan, 1994.
[10] 徐立鸿.预测控制的研究现状及问题.控制理论与应用.1994,11(1):121-125.
[11] Ohshima, M. Ohno, H. Hashimoto, I.. Model predictive control: Experiences in the university-industry joint projects and statistics on MPC applications in Japan. International workshop on predictive and receding horizon control, Korea, October, 1995.
[12] 李平,王树青,王骥程.预测控制研究的概况.化工自动化及仪表.1995,22(6):3-9.
[13] Mayne, D. Q.. Nonlinear model predictive control: An assessment. In J. C. Kantor, C. E. Garcia, B. Camahan (Eds.), Fifth international conference on chemical process control AICHE and CACHE, (pp. 217-231), 1997.
[14] Lee, J. H. Cooley, B.. Recent advances in model predictive control and other related areas. In J. C. Kantor, C. E. Garcia, B. Carnahan (Eds.), Fifth international conference on chemical process control AICHE and CACHE, (pp. 201-216b), 1997.
[15] Qin, S. J., Badgwell, T. A.. An overview of industrial model predictive control technology. In J. C. Kantor, C. E. Garcia, B. Carnahan (Eds.), Fifth international conference on chemical process control AICHE and CACHE, (pp. 232-256), 1997.
[16] Allgower, F., Badgwell, T. A., Qin, S. J., Rawlings, J. B., Wright, S. J.. Nonlinear predictive control and moving horizon estimation—an introductory overview. In P. M. Frank (Eds.), Advances in control: highlights of ECC'99. Berlin: Springer, 1999.
[17] 王献忠,杜维.模型预测控制发展概况.自动化与仪器仪表.1999,84(4):4-9.
[18] 梁春燕,谢剑英.预测控制中的若干问题研究.自动化与仪器仪表1999,84(4):10-13.
[19] Rawlings, J. B.. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 2000, 20:38-52.
[20] Mayne, D. Q., Rawlings, J. B., Rao, C. V., Scokaert, P. O. M.. Constrained model predictive control: Stability and optimality. Automatica, 2000, 36:789-814.
[21] Qin, S. J., Badgwell, T. A.. An overview of nonlinear model predictive control applications. In J. C. Kantor, C. E. Garcia, & B. Carnahan (Eds.), Nonlinear model predictive control. Basel: Birkhauser, 2000.
[22] Kulhavy, R., Lu, J., Samad, T.. Emerging technologies for enterprise optimization in the process industries. In chemical process control-6, assessment and new directions for research (CPC Ⅵ), Tuscon, Arizona, January 2001.
[23] Young, R. E., Bartusiak, R. B., Fontaine, R. B.. Evolution of an industrial nonlinear model predictive controller. In Chemical process control-CPC Ⅵ, Tucson, Arizona, CACHE, 2001, pp. 399-410.
[24] Downs, J. J.. Linking control strategy design and model predictive control. In Chemical process control-6, assessment and new directions for research (CPC Ⅵ), Tuscon, Arizona, January 2001.
[25] Qin, S. J., Badgwell, T. A.. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 733-764.
[26] 席裕庚.预测控制.北京:国防工业出版社,1993.
[27] 舒迪前.预测控制系统及其应用.北京:机械工业出版社,1996.
[28] Camacho, E. F., Bordons C.. Model Predictive Control. Springer, 1999.
[29] Allgower, F., Zheng, A., (Eds.). Nonlinear model predictive control progress in systems and control theory. Vol. 26. Basel, Boston, Berlin: Birkhauser Verlag, 1999.
[30] Kouvaritakis, B., Cannon, M. (Eds.). Nonlinear predictive control, theory and practice. London: The Institution of Electrical Engineers, 2001.
[31] Maciejowski, J. M.. Predictive control: with constraints. Pearson Education Limited, 2002.
[32] Kalman, R. E.. Contributions to the theory of optimal control. Bulletin de la Societe Mathematique de Mexicana, 1960, 5: 102-119.
[33] Kalman, R. E.. A new approach to linear filtering and prediction problems. Transactions of ASME, Journal of Basic Engineering, 1960, 87:35-45.
[34] Goodwin, G. C., Graebe, S. F., Salgado, M. E.. Control system design. Englewood Cliffs, NJ:Prentice Hall, 2001.
[35] Richalet, J., Rault, A., Testud, J. L., Papon, J.. Algorithmic control of industrial processed. In Proceedings of the 4th IFAC symposium on identification and system parameter estimation. (pp. 1119-1167), 1976.
[36] Prett, D. M., Gillette, R. D.. Optimization and constrained multivariable control of a catalytic cracking unit. In Proceedings of the joint automatic control conference, 1980.
[37] Propoi, A. I.. Use of linear programming methods for synthesizing sampled-data automatic systems. Automatic Remote Control, 1963, 24(7): 837-844.
[38] Lee, e. B., Markus, L.. Foundations of optimal control theory. New York: Wiley, 1967.
[39] Culter, C. R., Ramaker, B. L.. Dynamic matrix control-a computer control algorithm. AIChE national meeting, Houston, TX, April, 1979.
[40] Culter, C. R., Ramaker, B. L.. Dynamic matrix control-a computer control algorithm. In Proceeding of the joint automatic control conference, 1980.
[41] Culter, C. R., Morshedi, A., Haydel, J. An industrial perspective on advanced control. In AICHE annual meeting, Washington, DC, October, 1983.
[42] Garcia, C. E., Morshedi, A. M. Quadratic programming solution of dynamic matrix control (QDMC). Chemical Engineering Communications, 1986, 46: 73-87.
[43] Grosdidier, P., Froisy, B., Hammann, M.. The IDCOM-M controller. In T. J. McAvoy, Y. Arkun, E. Zafiriou (Eds.), Proceedings of the 1988 IFAC workshop on model based process control (pp. 31-36). Oxford: Pergamon Press, 1988.
[44] Froisy, J. B., Matsko, T.. IDCOM-M application to the shell fundamental control problem. AICHE annual meeting, November 1990.
[45] Marquis, P., Broustail, J. P.. SMOC, a bridge between state space and model predictive controllers: Application to the automation oof a hydrotreating unit. In T. J. McAvoy, Y. Arkun, E. Zafiriou (Eds.), Proceedings of the 1988 IFAC workshop on model based process control (pp. 27-43). Oxford: Pergamon Press, 1988.
[46] Yousfi, C, Tournier, R.. Steady-state optimization inside model predictive control. In Proceedings of ACC'91, Boston, MA (pp. 1866-1870), 1991.
[47] Richalet, J., Rault, A., Testud, J. L., Papon, J.. Model predictive heuristic control: Application to industrial processes. Automatica, 14: 413-428, 1978.
[48] Prett, D. M., Garcia, C. E.. Fundamental process control. Boston: Butterworths, 1988.
[49] DMC Corp. [DMC]TM. Technology overview. Product literature from DMC Corp., July 1994.
[50] Honeywell Inc.. RMPCT concepts reference. Product literature from Honeywell, Inc., October 1995.
[51] Richalet, J.. Industrial applications of model-based control. Automatica, 1993, 29:1251-1274.
[52] De Oliveira, N. M. C, Biegler, L. T.. Constraint handling and stability properties of model-predictive control. A.I.CH.E. Journal, 1994, 40(7): 1138-1155.
[53] De Oliveira, N. M. C, Biegler, L. T.. An extension of newtontype algorithms for nonlinear process control. Automatica, 1995, 31: 281-286.
[54] Sentoni, G. B., Biegler, L. T., Guiver, J. B., Zhao, H.. State-space nonlinear process modeling. A.I.CH.E Journal, 1998, 44(10): 2229-2239.
[55] Zhao, H., Guiver, J. P., Sentoni, G. B.. An identification approach to nonlinear state space model for industrial multivariable model predictive control. In Proceedings of the 1998 American control conference, Philadelphia, Pennsylvania (pp. 796-800), 1998.
[56] Zhao, H., Guiver, J., Neelakantan, R., Biegler, L. T.. A nonlinear industrial model predictive controller using integrated PLS and neural state space model. In IFAC 14' triennial world congress, Beijing, Peoples Republic of China, 1999.
[57] Berkowitz, P., Papadopoulos, M.. Multivariable process control method and apparatus. US Patent 5396416, 1995.
[58] MVC3.0 User Manual. Continental Controls, Inc. Product Literature, 1995.
[59] Berkowitz, P., Papadopoulos, ML, Colwell, L., Moran, M.. Multivariable process control method and apparatus. US Patent 5488561, 1996.
[60] Poe, W., Munsif, H.. Benefits of advanced process control and economic optimization to petrochemical processes. In Hydrocarbon processing's process optimization conference, Houston, TX, 1998.
[61] Bartusiak, R. D., Fontaine, R. W.. Feedback method ofr controlling non-linear processes. US Patent 5682309,1997.
[62] Demoro, E., Axelrud, C, Johnston, D., Martin, G. Neural network modeling and control of polypropylene process. In Society of plastics engineers international conference, Houston, TX, 1997.
[63] Keeler, J., Martin, G, Boe, G, Piche, S., Mathur, U., Johnston, D.. The process perfecter, the next step in multivariable control and optimization. Technical report, Pavilion Technologies, Inc., Austin, TX, 1996.
[64] Martin, G, Boe, G, Keeler, J., Timmer, D., Havener, J,, Method and apparatus for modeling dynamic and steady-state processes for prediction, control, and optimization. US Patent, 1998.
[65] Martin, G, Johnston, D.. Continuous model-based optimization. In Hydrocarbon processing's process optimization conference, Houston, TX, 1998.
[66] Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M.. Nonlinear model predictive control using neural networks. IEEE Control System Magazine, 2000, 20(3): 53-62.
[67] Lasdon, L. S., Warren, A. D.. GRG2 user's guide. Cleveland, OH: Cleveland State University, 1986.
[68] Scokaert, P. O. M., Rawlings, J. B.. Constrained linear quadratic regulation. IEEE Transactions on Automatic Control, 1998, 43(8): 1163-1169.
[69] Chen, H., Allgower, F.. A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica, 1995, 34(10): 1205-1218.
[70] Rao, C. V., Wright, S. J., Rawling, J. B.. Application in interior-point methods to model predictive control. Journal of Optimization Theory Application, 1998, 99: 723-757.
[71] Dollar, R., Melton, L. L., Morshedi, A. M., Glasgow, D. T., Repsher, K. W.. Consider adaptive multivariable predictive controllers. Hydrocarbon Processing, 1993, 10: 109-112.
[72] Foss, B. A., Lohmann, B., Marquardt, W.. A field study of the industrial modeling process. Journal of Process Control, 1998, 8(5,6): 325-338.
[73] Camacho, E. F., Berenguel, M., Bordons C.. Adaptive generalized predictive control of a distributed collector field. IEEE Tran. Control Systems Technology, 1994, 2: 462-468.
[74] Clarke, D. W. Application of generalized predictive control to industrial processes. IEEE Control Systems Magazine, 1988, 122: 49-55.
[75] Richalet, J., Abu el Ata-Doss, S., Arber, C., Kuntze, H. B.. Predictive functional control: application to fast and accurate robots. In Proc. IFAC World Congress, Munich, 1987.
[76] Clarke, D. W., Mohtadi, C., Tuffs, P. S.. Generalized Predictive Control. Part I. The Basic Algorithm. Automatica, 1987, 23(2): 137-148.
[77] Clarke, D. W., Mohtadi, C., Tuffs, P. S.. Generalized Predictive Control. Part Ⅱ. Extensions and Interpretations. Automatica, 1987, 23(2): 149-160.
[78] 徐立鸿,冯纯伯.论广义预测控制.控制与决策,1992,7(4):241-246.
[79] 谢克明,李国勇.广义预测控制技术的现状及展望.电力学报.,1994, 9(2):1—5.
[80] 王伟,杨建军.广义预测控制:理论、算法及应用.控制理论与应用1997,14(6):777-786.
[81] 王伟.广义预测控制理论及其应用.北京:科学出版社,1998.
[82] Ydstie, B. E.. Extended Horizon Adaptive Control. In Proc. 9th IFAC World Congress, Budapest, Hungary, 1984.
[83] 袁著祉.递推广义预测自校正控制器.自动化学报,1989,15(4):348-351.
[84] 徐立鸿,袁震东.ARMAX模型的递推广义预测控制算法.控制理论与应用1990,7(3):102-107.
[85] 郭庆鼎,金元郁,胡耀华.求解GPC中逆矩阵的递推算法.控制与决策,1996,11(4):510-513.
[86] 王一晶,左志强.一种新型广义预测控制快速算法.模型识别与人工智能2002,15(3):295-298.
[87] 徐仲,张凯院,陆全.TEOPLITZ矩阵类的快速算法.西安:西北工业大学出版社,1999.
[88] 金元郁,顾兴源.改进的广义预测控制算法.信息与控制,1990,19(3):8-14.
[89] 金元郁,顾兴源.改进的多变量广义预测控制算法.信息与控制,1990,19(6):20-22.
[90] 王伟.广义预测自适应控制的直接算法及全局收敛性分析.自动化学报,1995,21(1):57-62.
[91] 王伟.一种广义预测自适应控制的直接算法.自动化学报,1996,22(3):270-277.
[92] 郭健,陈庆伟,吴晓蓓,胡维礼,吴宏鑫.一类非线性系统的稳定自适应控制。控制理论与应用,2003,20(4):603-606.
[93] 王倩,荣德善.单变量隐式广义预测自校正控制算法.计算技术与自动化.1990,9(3):1-5.
[94] 王倩.多变量隐式广义预测自校正控制算法.计算技术与自动化.1992,11(2):37-42.
[95] 舒迪前,石中锁.隐式广义预测自校正控制器及其全局收敛性.自动化学报1995,21(5):545-554.
[96] 胡耀华,贾欣乐.广义预测控制的直接算法.控制与决策,2000,15(2):221-223.
[97] 王秩,席裕庚.广义预测控制的并行算法.第一届中国智能控制与智能 自动化学术会议论文集(CICIA94).沈阳:东北大学出版社,1994,463-468.
[98] 王秩,席裕庚.广义预测控制的并行算法.自动化学报,1996,22(1):74-78.
[99] 慕德俊,戴冠中.状念空间模型广义预测控制的并行算法.控制理论与应.用1995,12(5):646-652.
[100] 慕德俊.基于输入输出模型广义预测模型的并行算法.控制理论应用,1997,14(1):80-83.
[101] Camacho, E. F., Bordons, C.. Implementation of self tuning generalized predictive controllers for the process industry. International Journal of Adaptive Control and Signal Processing, 1993, 7(1): 63-73.
[102] 谢永斌,李琳,罗忠,冯祖仁,胡保生.利用小脑模型提前计算的广义预测控制.西安交通大学学报,1997,31(10):30-34.
[103] 谢永斌,罗忠,冯祖仁,胡保生.基于连续映射小脑模型的广义预测控制快速算法.控制理论与应用,1997,14(6):842-846.
[104] 李奇安,李平,李悦.基于BP网络的典型工业过程自适应预测区域控制.抚顺石油学院学报,2001,21(2):66-71.
[105] QI-AN LI, SHU-QING, WANG Fast Algorithm for Adaptive Generalized Predictive Control Based on BP Neural Networks. In Proceedings of the 2004 IEEE International Conference on Machine Learning and Cybernetics, Shanghai, pp.738-842, August 26-29, 2004.
[106] 袁著祉,崔保民.新型多变量广义预测自校正控制器.控制理论与应用,1992,9(6):672-677.
[107] 邢以群.管理学.杭州:浙江大学出版社,1997。
[108] 郑大钟.线性系统理论.北京:清华大学出版社,1990.
[109] 方崇智,萧德云.过程辨识北京:清华大学出版社,1988.
[110] Lennart Ljung. System identification-theory for the user. Tsinghua University Press, Beijing, 2002
[111] Lennart Ljung. System identification Toolbox for Use with MATLAB-User's Guide. The Math Works, 2002.
[112] Katsuhiko Ogata. Modern Control Engineering, Fourth Edition. Prentice Hall, 2002.
[113] Howard Demuth, Mark Beale. Neural Network Toolbox for Use with MATLAB-User's Guide. The Math Works, 2002.
[114] 李清泉.自适应控制统理论、设计与应用.北京:科学出版社,1990.
[115] 徐士良,孙甲松.科学计算通用程序集.沈阳:辽宁科学技术出版社,1997.
[116] JX-300X 集散控制系统使用手册-系统硬件.浙江浙大中控技术有限公司,2003.
[117] JX-300X 集散控制系统使用手册-系统组态.浙江浙大中控技术有限公司,2003.
[118] JX-300X 集散控制系统使用手册-实时监控.浙江浙大中控技术有限公司,2003.
[119] JX-300X 集散控制系统使用手册-SCX语言.浙江浙大中控技术有限公司,2003.
[120] JX-300X 集散控制系统使用手册-流程图绘制.浙江浙大中控技术有限公司,2003.
[121] Manfred Morari,N.Lawrence Richer.Model Predictive Control Toolbox for Use with Matlab.User's Guide Version Ⅰ.The Math Works.1998.
[122] 王建华,黄河清.计算机控制技术.北京:高等教育出版社,2003.
[123] 李奇安,李平,于海斌,王树青.串联系统的多前馈-反馈广义预测控制.控制与决策,2002,17(4)4:402-406.
[124] Alberto Bemporad,Manfred Morari,Vivek Dua,Efstratios N.Pistikopoulos. The explicit linear quadratic regulator for constrained systems.Automatica, 2002,38:3-20.
[2] Garcia, C. E., Prett, D. M., Morari, M. Model predictive control: Theory and practice—a survey. Automatica, 1989, 25(3):335-348.
[3] 席裕庚,许晓鸣,张钟俊.预测控制的研究现状和多层智能预测控制.控制理论与应用,1989,6(2):1-7.
[4] Richer, N. L.. Model predictive control: State of the art. In Y. Arkun, W. H. Ray (Eds.), Chemical process control—CPC Ⅳ, Fourth international conference on chemical process control (pp. 271-296). Amsterdam:Elsevier, 1991.
[5] Morari, M., Lee, J. H.. Model predictive control: The good, the bad, and the ugly. In Y. Arkun, W. H. Ray (Eds.), Chemical process control—CPC Ⅳ, Fourth international conference on chemical process control (pp. 419-444). Amsterdam:Elsevier, 1991.
[6] Muske, K. R. Rawlings, J. B.. Model predictive control with linear models. A.I. CH.E Jouranl, 1993, 39(2):262-287.
[7] Froisy, J. B.. Model predictive control: Past, present and future. ISA Transactions, 1994, 33:235-243.
[8] Hillestad, M., Andersen, K. S.. Model predictive control for grade transitions of a polypropylene reactor. In Proceedings of the 4th European symposium on computer aided process engineering (ESCAPE 4), Dublin, March 1994.
[9] Rawlings, J. B., Meadows, E. S., Muske, K.. Nonlinear model predictive control: a tutorial and survey. In Proceedings of IFAC ADCHEM, Japan, 1994.
[10] 徐立鸿.预测控制的研究现状及问题.控制理论与应用.1994,11(1):121-125.
[11] Ohshima, M. Ohno, H. Hashimoto, I.. Model predictive control: Experiences in the university-industry joint projects and statistics on MPC applications in Japan. International workshop on predictive and receding horizon control, Korea, October, 1995.
[12] 李平,王树青,王骥程.预测控制研究的概况.化工自动化及仪表.1995,22(6):3-9.
[13] Mayne, D. Q.. Nonlinear model predictive control: An assessment. In J. C. Kantor, C. E. Garcia, B. Camahan (Eds.), Fifth international conference on chemical process control AICHE and CACHE, (pp. 217-231), 1997.
[14] Lee, J. H. Cooley, B.. Recent advances in model predictive control and other related areas. In J. C. Kantor, C. E. Garcia, B. Carnahan (Eds.), Fifth international conference on chemical process control AICHE and CACHE, (pp. 201-216b), 1997.
[15] Qin, S. J., Badgwell, T. A.. An overview of industrial model predictive control technology. In J. C. Kantor, C. E. Garcia, B. Carnahan (Eds.), Fifth international conference on chemical process control AICHE and CACHE, (pp. 232-256), 1997.
[16] Allgower, F., Badgwell, T. A., Qin, S. J., Rawlings, J. B., Wright, S. J.. Nonlinear predictive control and moving horizon estimation—an introductory overview. In P. M. Frank (Eds.), Advances in control: highlights of ECC'99. Berlin: Springer, 1999.
[17] 王献忠,杜维.模型预测控制发展概况.自动化与仪器仪表.1999,84(4):4-9.
[18] 梁春燕,谢剑英.预测控制中的若干问题研究.自动化与仪器仪表1999,84(4):10-13.
[19] Rawlings, J. B.. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 2000, 20:38-52.
[20] Mayne, D. Q., Rawlings, J. B., Rao, C. V., Scokaert, P. O. M.. Constrained model predictive control: Stability and optimality. Automatica, 2000, 36:789-814.
[21] Qin, S. J., Badgwell, T. A.. An overview of nonlinear model predictive control applications. In J. C. Kantor, C. E. Garcia, & B. Carnahan (Eds.), Nonlinear model predictive control. Basel: Birkhauser, 2000.
[22] Kulhavy, R., Lu, J., Samad, T.. Emerging technologies for enterprise optimization in the process industries. In chemical process control-6, assessment and new directions for research (CPC Ⅵ), Tuscon, Arizona, January 2001.
[23] Young, R. E., Bartusiak, R. B., Fontaine, R. B.. Evolution of an industrial nonlinear model predictive controller. In Chemical process control-CPC Ⅵ, Tucson, Arizona, CACHE, 2001, pp. 399-410.
[24] Downs, J. J.. Linking control strategy design and model predictive control. In Chemical process control-6, assessment and new directions for research (CPC Ⅵ), Tuscon, Arizona, January 2001.
[25] Qin, S. J., Badgwell, T. A.. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 733-764.
[26] 席裕庚.预测控制.北京:国防工业出版社,1993.
[27] 舒迪前.预测控制系统及其应用.北京:机械工业出版社,1996.
[28] Camacho, E. F., Bordons C.. Model Predictive Control. Springer, 1999.
[29] Allgower, F., Zheng, A., (Eds.). Nonlinear model predictive control progress in systems and control theory. Vol. 26. Basel, Boston, Berlin: Birkhauser Verlag, 1999.
[30] Kouvaritakis, B., Cannon, M. (Eds.). Nonlinear predictive control, theory and practice. London: The Institution of Electrical Engineers, 2001.
[31] Maciejowski, J. M.. Predictive control: with constraints. Pearson Education Limited, 2002.
[32] Kalman, R. E.. Contributions to the theory of optimal control. Bulletin de la Societe Mathematique de Mexicana, 1960, 5: 102-119.
[33] Kalman, R. E.. A new approach to linear filtering and prediction problems. Transactions of ASME, Journal of Basic Engineering, 1960, 87:35-45.
[34] Goodwin, G. C., Graebe, S. F., Salgado, M. E.. Control system design. Englewood Cliffs, NJ:Prentice Hall, 2001.
[35] Richalet, J., Rault, A., Testud, J. L., Papon, J.. Algorithmic control of industrial processed. In Proceedings of the 4th IFAC symposium on identification and system parameter estimation. (pp. 1119-1167), 1976.
[36] Prett, D. M., Gillette, R. D.. Optimization and constrained multivariable control of a catalytic cracking unit. In Proceedings of the joint automatic control conference, 1980.
[37] Propoi, A. I.. Use of linear programming methods for synthesizing sampled-data automatic systems. Automatic Remote Control, 1963, 24(7): 837-844.
[38] Lee, e. B., Markus, L.. Foundations of optimal control theory. New York: Wiley, 1967.
[39] Culter, C. R., Ramaker, B. L.. Dynamic matrix control-a computer control algorithm. AIChE national meeting, Houston, TX, April, 1979.
[40] Culter, C. R., Ramaker, B. L.. Dynamic matrix control-a computer control algorithm. In Proceeding of the joint automatic control conference, 1980.
[41] Culter, C. R., Morshedi, A., Haydel, J. An industrial perspective on advanced control. In AICHE annual meeting, Washington, DC, October, 1983.
[42] Garcia, C. E., Morshedi, A. M. Quadratic programming solution of dynamic matrix control (QDMC). Chemical Engineering Communications, 1986, 46: 73-87.
[43] Grosdidier, P., Froisy, B., Hammann, M.. The IDCOM-M controller. In T. J. McAvoy, Y. Arkun, E. Zafiriou (Eds.), Proceedings of the 1988 IFAC workshop on model based process control (pp. 31-36). Oxford: Pergamon Press, 1988.
[44] Froisy, J. B., Matsko, T.. IDCOM-M application to the shell fundamental control problem. AICHE annual meeting, November 1990.
[45] Marquis, P., Broustail, J. P.. SMOC, a bridge between state space and model predictive controllers: Application to the automation oof a hydrotreating unit. In T. J. McAvoy, Y. Arkun, E. Zafiriou (Eds.), Proceedings of the 1988 IFAC workshop on model based process control (pp. 27-43). Oxford: Pergamon Press, 1988.
[46] Yousfi, C, Tournier, R.. Steady-state optimization inside model predictive control. In Proceedings of ACC'91, Boston, MA (pp. 1866-1870), 1991.
[47] Richalet, J., Rault, A., Testud, J. L., Papon, J.. Model predictive heuristic control: Application to industrial processes. Automatica, 14: 413-428, 1978.
[48] Prett, D. M., Garcia, C. E.. Fundamental process control. Boston: Butterworths, 1988.
[49] DMC Corp. [DMC]TM. Technology overview. Product literature from DMC Corp., July 1994.
[50] Honeywell Inc.. RMPCT concepts reference. Product literature from Honeywell, Inc., October 1995.
[51] Richalet, J.. Industrial applications of model-based control. Automatica, 1993, 29:1251-1274.
[52] De Oliveira, N. M. C, Biegler, L. T.. Constraint handling and stability properties of model-predictive control. A.I.CH.E. Journal, 1994, 40(7): 1138-1155.
[53] De Oliveira, N. M. C, Biegler, L. T.. An extension of newtontype algorithms for nonlinear process control. Automatica, 1995, 31: 281-286.
[54] Sentoni, G. B., Biegler, L. T., Guiver, J. B., Zhao, H.. State-space nonlinear process modeling. A.I.CH.E Journal, 1998, 44(10): 2229-2239.
[55] Zhao, H., Guiver, J. P., Sentoni, G. B.. An identification approach to nonlinear state space model for industrial multivariable model predictive control. In Proceedings of the 1998 American control conference, Philadelphia, Pennsylvania (pp. 796-800), 1998.
[56] Zhao, H., Guiver, J., Neelakantan, R., Biegler, L. T.. A nonlinear industrial model predictive controller using integrated PLS and neural state space model. In IFAC 14' triennial world congress, Beijing, Peoples Republic of China, 1999.
[57] Berkowitz, P., Papadopoulos, M.. Multivariable process control method and apparatus. US Patent 5396416, 1995.
[58] MVC3.0 User Manual. Continental Controls, Inc. Product Literature, 1995.
[59] Berkowitz, P., Papadopoulos, ML, Colwell, L., Moran, M.. Multivariable process control method and apparatus. US Patent 5488561, 1996.
[60] Poe, W., Munsif, H.. Benefits of advanced process control and economic optimization to petrochemical processes. In Hydrocarbon processing's process optimization conference, Houston, TX, 1998.
[61] Bartusiak, R. D., Fontaine, R. W.. Feedback method ofr controlling non-linear processes. US Patent 5682309,1997.
[62] Demoro, E., Axelrud, C, Johnston, D., Martin, G. Neural network modeling and control of polypropylene process. In Society of plastics engineers international conference, Houston, TX, 1997.
[63] Keeler, J., Martin, G, Boe, G, Piche, S., Mathur, U., Johnston, D.. The process perfecter, the next step in multivariable control and optimization. Technical report, Pavilion Technologies, Inc., Austin, TX, 1996.
[64] Martin, G, Boe, G, Keeler, J., Timmer, D., Havener, J,, Method and apparatus for modeling dynamic and steady-state processes for prediction, control, and optimization. US Patent, 1998.
[65] Martin, G, Johnston, D.. Continuous model-based optimization. In Hydrocarbon processing's process optimization conference, Houston, TX, 1998.
[66] Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M.. Nonlinear model predictive control using neural networks. IEEE Control System Magazine, 2000, 20(3): 53-62.
[67] Lasdon, L. S., Warren, A. D.. GRG2 user's guide. Cleveland, OH: Cleveland State University, 1986.
[68] Scokaert, P. O. M., Rawlings, J. B.. Constrained linear quadratic regulation. IEEE Transactions on Automatic Control, 1998, 43(8): 1163-1169.
[69] Chen, H., Allgower, F.. A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica, 1995, 34(10): 1205-1218.
[70] Rao, C. V., Wright, S. J., Rawling, J. B.. Application in interior-point methods to model predictive control. Journal of Optimization Theory Application, 1998, 99: 723-757.
[71] Dollar, R., Melton, L. L., Morshedi, A. M., Glasgow, D. T., Repsher, K. W.. Consider adaptive multivariable predictive controllers. Hydrocarbon Processing, 1993, 10: 109-112.
[72] Foss, B. A., Lohmann, B., Marquardt, W.. A field study of the industrial modeling process. Journal of Process Control, 1998, 8(5,6): 325-338.
[73] Camacho, E. F., Berenguel, M., Bordons C.. Adaptive generalized predictive control of a distributed collector field. IEEE Tran. Control Systems Technology, 1994, 2: 462-468.
[74] Clarke, D. W. Application of generalized predictive control to industrial processes. IEEE Control Systems Magazine, 1988, 122: 49-55.
[75] Richalet, J., Abu el Ata-Doss, S., Arber, C., Kuntze, H. B.. Predictive functional control: application to fast and accurate robots. In Proc. IFAC World Congress, Munich, 1987.
[76] Clarke, D. W., Mohtadi, C., Tuffs, P. S.. Generalized Predictive Control. Part I. The Basic Algorithm. Automatica, 1987, 23(2): 137-148.
[77] Clarke, D. W., Mohtadi, C., Tuffs, P. S.. Generalized Predictive Control. Part Ⅱ. Extensions and Interpretations. Automatica, 1987, 23(2): 149-160.
[78] 徐立鸿,冯纯伯.论广义预测控制.控制与决策,1992,7(4):241-246.
[79] 谢克明,李国勇.广义预测控制技术的现状及展望.电力学报.,1994, 9(2):1—5.
[80] 王伟,杨建军.广义预测控制:理论、算法及应用.控制理论与应用1997,14(6):777-786.
[81] 王伟.广义预测控制理论及其应用.北京:科学出版社,1998.
[82] Ydstie, B. E.. Extended Horizon Adaptive Control. In Proc. 9th IFAC World Congress, Budapest, Hungary, 1984.
[83] 袁著祉.递推广义预测自校正控制器.自动化学报,1989,15(4):348-351.
[84] 徐立鸿,袁震东.ARMAX模型的递推广义预测控制算法.控制理论与应用1990,7(3):102-107.
[85] 郭庆鼎,金元郁,胡耀华.求解GPC中逆矩阵的递推算法.控制与决策,1996,11(4):510-513.
[86] 王一晶,左志强.一种新型广义预测控制快速算法.模型识别与人工智能2002,15(3):295-298.
[87] 徐仲,张凯院,陆全.TEOPLITZ矩阵类的快速算法.西安:西北工业大学出版社,1999.
[88] 金元郁,顾兴源.改进的广义预测控制算法.信息与控制,1990,19(3):8-14.
[89] 金元郁,顾兴源.改进的多变量广义预测控制算法.信息与控制,1990,19(6):20-22.
[90] 王伟.广义预测自适应控制的直接算法及全局收敛性分析.自动化学报,1995,21(1):57-62.
[91] 王伟.一种广义预测自适应控制的直接算法.自动化学报,1996,22(3):270-277.
[92] 郭健,陈庆伟,吴晓蓓,胡维礼,吴宏鑫.一类非线性系统的稳定自适应控制。控制理论与应用,2003,20(4):603-606.
[93] 王倩,荣德善.单变量隐式广义预测自校正控制算法.计算技术与自动化.1990,9(3):1-5.
[94] 王倩.多变量隐式广义预测自校正控制算法.计算技术与自动化.1992,11(2):37-42.
[95] 舒迪前,石中锁.隐式广义预测自校正控制器及其全局收敛性.自动化学报1995,21(5):545-554.
[96] 胡耀华,贾欣乐.广义预测控制的直接算法.控制与决策,2000,15(2):221-223.
[97] 王秩,席裕庚.广义预测控制的并行算法.第一届中国智能控制与智能 自动化学术会议论文集(CICIA94).沈阳:东北大学出版社,1994,463-468.
[98] 王秩,席裕庚.广义预测控制的并行算法.自动化学报,1996,22(1):74-78.
[99] 慕德俊,戴冠中.状念空间模型广义预测控制的并行算法.控制理论与应.用1995,12(5):646-652.
[100] 慕德俊.基于输入输出模型广义预测模型的并行算法.控制理论应用,1997,14(1):80-83.
[101] Camacho, E. F., Bordons, C.. Implementation of self tuning generalized predictive controllers for the process industry. International Journal of Adaptive Control and Signal Processing, 1993, 7(1): 63-73.
[102] 谢永斌,李琳,罗忠,冯祖仁,胡保生.利用小脑模型提前计算的广义预测控制.西安交通大学学报,1997,31(10):30-34.
[103] 谢永斌,罗忠,冯祖仁,胡保生.基于连续映射小脑模型的广义预测控制快速算法.控制理论与应用,1997,14(6):842-846.
[104] 李奇安,李平,李悦.基于BP网络的典型工业过程自适应预测区域控制.抚顺石油学院学报,2001,21(2):66-71.
[105] QI-AN LI, SHU-QING, WANG Fast Algorithm for Adaptive Generalized Predictive Control Based on BP Neural Networks. In Proceedings of the 2004 IEEE International Conference on Machine Learning and Cybernetics, Shanghai, pp.738-842, August 26-29, 2004.
[106] 袁著祉,崔保民.新型多变量广义预测自校正控制器.控制理论与应用,1992,9(6):672-677.
[107] 邢以群.管理学.杭州:浙江大学出版社,1997。
[108] 郑大钟.线性系统理论.北京:清华大学出版社,1990.
[109] 方崇智,萧德云.过程辨识北京:清华大学出版社,1988.
[110] Lennart Ljung. System identification-theory for the user. Tsinghua University Press, Beijing, 2002
[111] Lennart Ljung. System identification Toolbox for Use with MATLAB-User's Guide. The Math Works, 2002.
[112] Katsuhiko Ogata. Modern Control Engineering, Fourth Edition. Prentice Hall, 2002.
[113] Howard Demuth, Mark Beale. Neural Network Toolbox for Use with MATLAB-User's Guide. The Math Works, 2002.
[114] 李清泉.自适应控制统理论、设计与应用.北京:科学出版社,1990.
[115] 徐士良,孙甲松.科学计算通用程序集.沈阳:辽宁科学技术出版社,1997.
[116] JX-300X 集散控制系统使用手册-系统硬件.浙江浙大中控技术有限公司,2003.
[117] JX-300X 集散控制系统使用手册-系统组态.浙江浙大中控技术有限公司,2003.
[118] JX-300X 集散控制系统使用手册-实时监控.浙江浙大中控技术有限公司,2003.
[119] JX-300X 集散控制系统使用手册-SCX语言.浙江浙大中控技术有限公司,2003.
[120] JX-300X 集散控制系统使用手册-流程图绘制.浙江浙大中控技术有限公司,2003.
[121] Manfred Morari,N.Lawrence Richer.Model Predictive Control Toolbox for Use with Matlab.User's Guide Version Ⅰ.The Math Works.1998.
[122] 王建华,黄河清.计算机控制技术.北京:高等教育出版社,2003.
[123] 李奇安,李平,于海斌,王树青.串联系统的多前馈-反馈广义预测控制.控制与决策,2002,17(4)4:402-406.
[124] Alberto Bemporad,Manfred Morari,Vivek Dua,Efstratios N.Pistikopoulos. The explicit linear quadratic regulator for constrained systems.Automatica, 2002,38:3-20.