A survey of industrial model predictive control technology

Control Engineering Practice - Tập 11 Số 7 - Trang 733-764 - 2003
S. Joe Qin1, Thomas A. Badgwell2
1Department of Chemical Engineering, The University of Texas at Austin, 1 Texas Lonhorns, C0400, Austin, TX 78712, USA
2Aspen Technology, Inc., 1293 Eldridge Parkway, Houston, TX 77077, USA

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Tài liệu tham khảo

Allgower, 1999, Nonlinear predictive control and moving horizon estimation—an introductory overview

Allgower, F., & Zheng, A., (Eds.). (2000). Nonlinear model predictive control, progress in systems and control theory. Vol. 26. Basel, Boston, Berlin: Birkhauser Verlag.

Bartusiak, R. D., & Fontaine, R. W. (1997). Feedback method for controlling non-linear processes. US Patent 5682309.

Berkowitz, P., & Papadopoulos, M. (1995). Multivariable process control method and apparatus. US Patent 5396416.

Berkowitz, P., Papadopoulos, M., Colwell, L., & Moran, M. (1996). Multivariable process control method and apparatus. US Patent 5488561.

Chen, 1995, A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability, Automatica, 34, 1205, 10.1016/S0005-1098(98)00073-9

Clarke, 1987, Generalized predictive control—Part I. The basic algorithm, Automatica, 23, 137, 10.1016/0005-1098(87)90087-2

MVC 3.0 User Manual (1995). Continental Controls, Inc. Product Literature.

Cutler, C., Morshedi, A., & Haydel, J. (1983). An industrial perspective on advanced control. In AICHE annual meeting, Washington, DC, October 1983.

Cutler, C. R., & Ramaker, B. L. (1979). Dynamic matrix control—a computer control algorithm. AICHE national meeting, Houston, TX, April 1979.

Cutler, C. R., & Ramaker, B. L. (1980). Dynamic matrix control—a computer control algorithm. In Proceedings of the joint automatic control conference.

Cutler, C. R., & Yocum, F. H. (1991). Experience with the DMC inverse for identification. In Y. Arkun, W. H. Ray (Eds.), Chemical process control—CPC IV. Fourth international conference on chemical process control (pp. 297–317). Amsterdam: Elsevier.

Demoro, E., Axelrud, C., Johnston, D., & Martin, G. (1997). Neural network modeling and control of polypropylene process. In Society of plastics engineers international conference, Houston, TX.

DMC Corp. [DMC]TM (1994). Technology overview. Product literature from DMC Corp., July 1994.

Dollar, 1993, Consider adaptive multivariable predictive controllers, Hydrocarbon Processing, 10, 109

Downs, J. J. (2001). Linking control strategy design and model predictive control. In Preprints: Chemical process control-6, assessment and new directions for research (CPC VI), Tuscon, AZ, January 2001 (pp. 411–422).

Foss, 1998, A field study of the industrial modeling process, Journal of Process Control, 8, 325, 10.1016/S0959-1524(98)00018-3

Froisy, 1994, Model predictive control, ISA Transactions, 33, 235, 10.1016/0019-0578(94)90095-7

Froisy, J. B., & Matsko, T. (1990). IDCOM-M application to the Shell fundamental control problem. AICHE annual meeting, November 1990.

Garcı́a, 1986, Quadratic programming solution of dynamic matrix control (QDMC), Chemical Engineering Communications, 46, 73, 10.1080/00986448608911397

Garcı́a, 1989, Model predictive control, Automatica, 25, 335, 10.1016/0005-1098(89)90002-2

Goodwin, 2001

Grosdidier, P., Froisy, B., & Hammann, M. (1988). 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.

Hillestad, M., & Andersen, K. S. (1994). 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.

Honeywell Inc. (1995). RMPCT concepts reference. Product literature from Honeywell, Inc., October 1995.

Kailath, 1980

Kailath, 2000

Kalman, 1960, Contributions to the theory of optimal control, Bulletin de la Societe Mathematique de Mexicana., 5, 102

Kalman, 1960, A new approach to linear filtering and prediction problems, Transactions of ASME, Journal of Basic Engineering, 87, 35, 10.1115/1.3662552

Kassmann, 2000, Robust steady-state target calculation for model predictive control, A.I.CH.E. Journal, 46, 1007, 10.1002/aic.690460513

Keeler, J., Martin, G., Boe, G., Piche, S., Mathur, U., & Johnston, D. (1996). The process perfecter: the next step in multivariable control and optimization. Technical report, Pavilion Technologies, Inc., Austin, TX.

Kothare, 1996, Robust constrained model predictive control using linear matrix inequalities, Automatica, 32, 1361, 10.1016/0005-1098(96)00063-5

Kouvaritakis, B., & Cannon, M. (Eds.) (2001). Nonlinear predictive control, theory and practice. London: The IEE.

Kulhavy, R., Lu, J., & Samad, T. (2001). Emerging technologies for enterprise optimization in the process industries. In Preprints: chemical process control-6, assessment and new directions for research (CPC VI), Tuscon, Arizona, January 2001 (pp. 411–422).

Kwakernaak, 1972

Larimore, W. E. (1990). Canonical variate analysis in identification, filtering and adaptive control. In Proceedings of the 29th conference on decision and control (pp. 596–604).

Lasdon, 1986

Lee, J. H., & Cooley, B. (1997). 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).

Lee, 1967

Lee, 1994, State-space interpretation of model predictive control, Automatica, 30, 707, 10.1016/0005-1098(94)90159-7

Lewis, 1991, Application of predictive control techniques to a distillation column, Journal of Process Control, 1, 17, 10.1016/0959-1524(91)85010-G

Ljung, 1987

Ljung, 1999

Maciejowski, 2002

Marquis, P., & Broustail, J. P. (1998). SMOC, a bridge between state space and model predictive controllers: Application to the automation of a hydrotreating unit. In T. J. McAvoy, Y. Arkun, & E. Zafiriou (Eds.), Proceedings of the 1988 IFAC workshop on model based process control (pp. 37–43). Oxford: Pergamon Press.

Martin, G., & Boe, G., Keeler, J., Timmer, D., Havener, J. (1998). Method and apparatus for modeling dynamic and steady-state processes for prediction, control, and optimization. US Patent.

Martin, G., & Johnston, D. (1998). Continuous model-based optimization. In Hydrocarbon processing's process optimization conference, Houston, TX.

Mayne, D. Q. (1997). Nonlinear model predictive control: An assessment. In J. C. Kantor, C. E. Garcia, & B. Carnahan (Eds.), Fifth international conference on chemical process control, AICHE and CACHE, (pp. 217–231).

Mayne, 2000, Constrained model predictive control, Automatica, 36, 789, 10.1016/S0005-1098(99)00214-9

Morari, M., & Lee, J. H. (1991). 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.

Morari, 1989

Muske, K. R. (1995). Linear model predictive control of chemical processes. PhD thesis, The University of Texas at Austin.

Muske, 1993, Model predictive control with linear models, A.I.CH.E Journal, 39, 262, 10.1002/aic.690390208

Ohshima, M., Ohno, H., & Hashimoto, I. (1995). 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.

De Oliveira, 1995, An extension of newtontype algorithms for nonlinear process control, Automatica, 31, 281, 10.1016/0005-1098(94)00086-X

De Oliveira, 1994, Constraint handling and stability properties of model-predictive control, A.I.CH.E Journal, 40, 1138, 10.1002/aic.690400706

Piche, S., Sayyar-Rodsari, B., Johnson, D., & Gerules, M. (2000). Nonlinear model predictive control using neural networks. IEEE Control Systems Magazine 53–62.

Poe, W., & Munsif, H. (1998). Benefits of advanced process control and economic optimization to petrochemical processes. In Hydrocarbon processing’s process optimization conference, Houston, TX.

Prett, 1988

Prett, D. M., & Gillette, R. D. (1980). Optimization and constrained multivariable control of a catalytic cracking unit. In Proceedings of the joint automatic control conference.

Propoi, 1963, Use of linear programming methods for synthesizing sampled-data automatic systems, Automatic Remote Control, 24, 837

Qin, S. J., & Badgwell, T. A. (1996). An overview of industrial model predictive control technology. In Chemical process control—CPC V, CACHE. Tahoe City, CA, January 1996.

Qin, S. J., & Badgwell, T. A. (1997). An overview of industrial model predictive control technology. In J. C. Kantor, C. E. Garcia, & B. Carnahan (Eds.), Chemical process control—V, Fifth international conference on chemical process control CACHE and AICHE, (pp. 232–256).

Qin, S. J., & Badgwell, T. A. (1998). An overview of nonlinear model predictive control applications. In F. Algower, & A. Zheng (Eds.), Nonlinear model predictive control workshop—assessment and future directions, Ascona, Switzerland.

Qin, 2000, An overview of nonlinear model predictive control applications

Rao, 1999, Steady states and constraints in model predictive control, A.I.CH.E. Journal, 45, 1266, 10.1002/aic.690450612

Rao, 1998, Application of interior-point methods to model predictive control, Journal of Optimization Theory Application, 99, 723, 10.1023/A:1021711402723

Rawlings, 2000, Tutorial overview of model predictive control, IEEE Control Systems Magazine, 20, 38, 10.1109/37.845037

Rawlings, J. B., Meadows, E. S., & Muske, K. (1994). Nonlinear model predictive control: a tutorial and survey. In Proceedings of IFAC ADCHEM, Japan.

Rawlings, 1993, Stability of constrained receding horizon control, IEEE Transactions on Automatic Control, 38, 1512, 10.1109/9.241565

Richalet, 1993, Industrial applications of model-based control, Automatica, 29, 1251, 10.1016/0005-1098(93)90049-Y

Richalet, J., Rault, A., Testud, J. L., & Papon, J. (1976). Algorithmic control of industrial processes. In Proceedings of the 4th IFAC symposium on identification and system parameter estimation. (pp. 1119–1167).

Richalet, 1978, Model predictive heuristic control, Automatica, 14, 413, 10.1016/0005-1098(78)90001-8

Ricker, N. L. (1991). 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.

Scokaert, 1998, Min–max feedback model predictive control for constrained linear systems, IEEE Transactions on Automatic Control, 43, 1136, 10.1109/9.704989

Scokaert, 1998, Constrained linear quadratic regulation, IEEE Transactions on Automatic Control, 43, 1163, 10.1109/9.704994

Sentoni, 1998, State-space nonlinear process modeling, A.I.CH.E Journal, 44, 2229, 10.1002/aic.690441011

Setpoint Inc. (1993). SMC-IDCOM: A state-of-the-art multivariable predictive controller. Product literature from Setpoint, Inc., October 1993.

Shinskey, 1994

Vuthandam, 1995, Performance bounds for robust quadratic dynamic matrix control with end condition, A.I.CH.E Journal, 41, 2083, 10.1002/aic.690410908

Young, R. E., Bartusiak, R. B., & Fontaine, R. B. (2001). Evolution of an industrial nonlinear model predictive controller. In Preprints: Chemical process control—CPC VI, Tucson, Arizona (pp. 399–410). CACHE.

Yousfi, C., & Tournier, R. (1991). Steady-state optimization inside model predictive control In Proceedings of ACC’91, Boston, MA (pp. 1866–1870).

Zafiriou, 1990, Robust model predictive control of processes with hard constraints, Computers in Chemical Engineering, 14, 359, 10.1016/0098-1354(90)87012-E

Zhao, H., Guiver, J., Neelakantan, R., & Biegler, L. T. (1999). A nonlinear industrial model predictive controller using integrated PLS and neural state space model. In IFAC 14th triennial world congress, Beijing, Peoples Republic of China.

Zhao, H., Guiver, J. P., & Sentoni, G. B. (1998). An identification approach to nonlinear state space model for industrial multivariable model predictive control. In Proceedings of the 1998 American control conference, Philadelphia, Pennsylvannia (pp. 796–800).