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Model Predictive Control

  • Jean-Pierre Corriou
Chapter

Abstract

Model predictive control (MPC) is widely used in the industry, and many references to industrial experience will serve to present the main characteristics of this important control approach. Furthermore, the method of dynamic matrix control, about which much literature is available, will be discussed with some detail. Finally, some general aspects of nonlinear model predictive control will be outlined.

Keywords

Model Predictive Control Reference Trajectory Fluid Catalytic Crack Prediction Horizon Manipulate Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. A. Al-Ghazzawi, E. Ali, A. Nouh, and E. Zafiriou. On-line tuning strategy for model predictive controllers. J. Proc. Cont, 11: 265–284, 2001.CrossRefGoogle Scholar
  2. H. Ali, S. Rohani, and J.P. Corriou. Modelling and control of a riser type fluid catalytic cracking (FCC) unit. Trans. IChemE, 75, part A: 401–412, 1997.Google Scholar
  3. H.K.A. Ali. Dynamic Modeling and Control of a Riser- Type Fluid Catalytic Cracking Unit. Phd thesis, University of Saskatchewan, Saskatoon, Canada, 1995.Google Scholar
  4. F. Allgöwer, T.A. Badgwell, J.S. Qin, J.B. Rawlings, and S.J. Wright. Nonlinear predictive control and moving horizon estimation–An introductory overview. In P. M. Frank, editor, Advances in Control, pages 391–449. Springer-Verlag, Berlin, 1999.CrossRefGoogle Scholar
  5. R.M. Ansari and M.O. Tadé. Constrained nonlinear multivariable control of a fluid catalytic cracking process. J. Proc. Cont, 10: 539–555, 1997.CrossRefGoogle Scholar
  6. J.M. Arandes and H.I. de Lasa. Simulation and multiplicity of steady states in fluidized FCCUs. Chem. Eng. Sci, 47 (9–11): 2535–2540, 1992.Google Scholar
  7. A. Arbel, Z. Huang, I.H. Rinard, R. Shinnar, and A.V. Sapre. Dynamic and control of fluidized catalytic crackers. 1. Modelling of the current generation of FCC’s. Ind. Eng. Chem. Res, 34: 1228–1243, 1995a.CrossRefGoogle Scholar
  8. A. Arbel, Z. Huang, I.H. Rinard, R. Shinnar, and A.V. Sapre. Dynamic and control of fluidized catalytic crackers. 2. Multiple steady states and instabilities. Ind. Eng. Chem. Res, 34: 3014–3026, 1995b.CrossRefGoogle Scholar
  9. A. Arbel, I.H. Rinard, and R. Shinnar. Dynamic and control of fluidized catalytic crackers. 3. Designing the control system: choice of manipulated and measured variables for partial control. Ind. Eng. Chem. Res, 35: 2215–2233, 1996.CrossRefGoogle Scholar
  10. A.A. Avidan and R. Shinnar. Development of catalytic cracking technology. a lesson in chemical reactor design. Ind. Eng. Chem. Res, 29: 931–942, 1990.CrossRefGoogle Scholar
  11. J.S. Balchen, D. Ljungquist, and S. Strand. State-space predictive control. Chem. Eng. Sci, 47 (4): 787–807, 1992.CrossRefGoogle Scholar
  12. B.W. Bequette. Nonlinear control of chemical processes: a review. Ind. Eng. Chem. Res, 30: 1391–1413, 1991.CrossRefGoogle Scholar
  13. L. Biegler. Efficient solution of dynamic optimization and NMPC problems. In F. Allgöwer and A. Zheng, editors, Nonlinear Predictive Control. Birkhäuser, 2000.Google Scholar
  14. R. R. Bitmead, M. Gevers, and V. Wertz. Adaptive Optimal Control, The Thinking Man’s GPC. Prentice Hall, New York, 1990.Google Scholar
  15. H. G. Bock, M. Diehl, J.P. Schlöder, F. Allgöwer, R. Findeisen, and Z. Nagy. Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. In International Symposium on Advanced Control of Chemical Processes, pages 695–702, Pisa Italy, 2000.Google Scholar
  16. E.F. Camacho and C. Bordons. Model Predictive Control in the Process Industry. Springer-Verlag, Berlin, 1995.CrossRefGoogle Scholar
  17. E.F. Camacho and C. Bordons. Model Predictive Control. Springer-Verlag, Berlin, 1998.Google Scholar
  18. H. Chen. Stability and Robustness Considerations in Nonlinear Model Predictive Control. PhD thesis, Stuttgart University, 1997.Google Scholar
  19. H. Chen and F. Allgöwer. A computationally attractive nonlinear predictive control scheme with guaranteed stability for stable systems. J. Proc. Cont, 8 (5–6): 475–485, 1998a.CrossRefGoogle Scholar
  20. H. Chen and F. Allgöwer. Nonlinear model predictive control schemes with guaranteed stability. In R. Berber and C. Kravaris, editors, Nonlinear Model Based Process Control, pages 465–494. Kluwer Academic Press, Netherlands, 1998b.CrossRefGoogle Scholar
  21. H. Chen and F. Allgöwer. A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica, 34 (10): 1205–1218, 1998e.MathSciNetMATHCrossRefGoogle Scholar
  22. P.D. Christofides and P. Daoutidis. Robust control of multivariable two-time scale nonlinear systems. J. Proc. Cont, 7 (5): 313–328, 1997.CrossRefGoogle Scholar
  23. D.W. Clarke. Advances in Model-Based Predictive Control. Oxford University Press, Oxford, 1994.Google Scholar
  24. D.W. Clarke, C. Mohtadi, and P.S. Tuffs. Generalized predictive control - PartGoogle Scholar
  25. I. The basic algorithm. Automatica, 23 (2): 137–148, 1987a.Google Scholar
  26. D.W. Clarke, C. Mohtadi, and P.S. Tuffs. Generalized predictive control - PartGoogle Scholar
  27. II. Extensions and interpretations. Automatica, 23 (2): 149–160, 1987b.Google Scholar
  28. C.R. Cutler and B.L. Ramaker. Dynamic matrix control - a computer control algorithm. In AIChE Annual Meeting, Houston, Texas, 1979.Google Scholar
  29. H. Demircioglu and P.J. Gawthrop. Continuous-time generalized predictive control (CGPC). Automatica, 27 (1): 55–74, 1991.MathSciNetMATHCrossRefGoogle Scholar
  30. H. Demircioglu and P.J. Gawthrop. Multivariable continuous-time generalized predictive control (MCGPC). Automatica, 28 (4): 697–713, 1992.MathSciNetMATHCrossRefGoogle Scholar
  31. M. Diehl, I. Uslu, R. Findeisen, S. Schwartzkopf, F. Allgöwer, H.G. Bock andT. Bürner, E.D. Gilles, A. Kienle, J.P. Schlöder, and E. Stein. Real-time optimization for large scale processes: nonlinear model predictive control of a high-purity distillation column. In Groeschel, Krumke, and Rambau, editors, Online Optimization of Large Scale Systems: State of the Art, pages 363–383. Springer, 2001.Google Scholar
  32. F.J. Doyle, B.A. Ogunnaike, and R.K. Pearson. Nonlinear model based control using second order Volterra models. Automatica, 31 (5): 697–714, 1995.MathSciNetMATHCrossRefGoogle Scholar
  33. F.J. Doyle, J.F. Pekny, P. Dave, and S. Bose. Specialized programming methods in the model predictive control of large-scale systems. Comp. Chem. Engng, 21: s847–852, 1997.Google Scholar
  34. S.S.E.H. Elnashaie and S.S. Elshishini. Digital simulation of industrial fluid catalytic cracking units-IV dynamic behaviour. Chem. Eng. Sci, 1993.Google Scholar
  35. S.S. Elshishini and S.S.E.H. Elnashaie. Digital simulation of industrial fluid catalytic cracking units: bifurcation and its implications. Chem. Eng. Sci, 1990.Google Scholar
  36. A.F. Errazu, H.I. de Lasa, and F. Sarti. A fluidized bed catalytic cracking regenerator model grid effects. Can. J. Chem. Engng, 57: 191–197, 1979.CrossRefGoogle Scholar
  37. R. Fletcher. Practical Methods of Optimization. Wiley, Chichester, 1991.Google Scholar
  38. J.B. Froisy. Model predictive control: past, present and future. ISA Transactions, 33: 235–243, 1994.CrossRefGoogle Scholar
  39. C.E. Garcia. Quadratic dynamic matrix control of nonlinear processes - an application to a batch reaction process. In AIChE Annual Meeting, San Franciso, USA, 1984.Google Scholar
  40. C.E. Garcia and M. Morari. Internal model control. 1. A unifying review and some new results. Ind. Eng. Chem. Process Des. Dev, 21: 308–323, 1982.CrossRefGoogle Scholar
  41. C.E. Garcia and A.M. Morshedi. Quadratic programming solution of dynamic matrix control (QDMC). Chem. Eng. Comm, 46: 73–87, 1986.CrossRefGoogle Scholar
  42. C.E. Garcia, D.M. Prett, and M. Morari. Model predictive control: Theory and practice–a survey. Automatica, 25 (3): 335–348, 1989.MATHCrossRefGoogle Scholar
  43. G. Gattu and E. Zafiriou. Nonlinear quadratic dynamic matrix control with state estimation. Ind. Eng. Chem. Res, 31: 1096–1104, 1992.CrossRefGoogle Scholar
  44. G. Gattu and E. Zafiriou. Observer based nonlinear quadratic dynamic matrix control for state space and input/output models. Can. J. Chem. Eng, 73: 883–895, 1995.CrossRefGoogle Scholar
  45. H. Genceli and M. Nikolaou. Robust stability analysis of constrained li-norm model predictive control. AIChE J, 39 (12): 1954–1965, 1993.MathSciNetCrossRefGoogle Scholar
  46. H. Genceli and M. Nikolaou. Design of robust constrained model-predictive controllers with Volterra series. AIChE J, 41 (9): 2098–2107, 1995.MathSciNetCrossRefGoogle Scholar
  47. P. Grosdidier, A. Mason, A. Aitolahti, P. Heinonen, and V. Vanhamäki. FCC unit reactor-regenerator control. Comp. Chem. Engng, 17 (2): 165–179, 1993.CrossRefGoogle Scholar
  48. P.H. Gusciora, J.H. McAmis, R.C. Sorensen, and C.R. Cutler. Experiences applying DMC on a model IV FCC. In paper 131L, AIChE meeting, Miami, Fl, 1992.Google Scholar
  49. I.S. Han and C.B. Chung. Dynamic modeling and simulation of a fluidized catalytic cracking process. Part I: Process modeling. Chem. Eng. Sci, 56: 1951–1971, 2001a.CrossRefGoogle Scholar
  50. I.S. Han and C.B. Chung. Dynamic modeling and simulation of a fluidized catalytic cracking process. Part II: Property estimation and simulation. Chem. Eng. Sci, 56: 1973–1990, 2001b.CrossRefGoogle Scholar
  51. M. Hovd and S. Skogestad. Procedure for regulatory control structure selection with application to the FCC process. AIChE J, 39 (12): 1938–1953, 1993.CrossRefGoogle Scholar
  52. R.M.C. De Keyser and A.R. Van Cauwenberghe. Extended prediction self-adaptive control. In 7th IFAC Symposium on Identification and System Parameter Estimation,pages 1255–1260, Oxford, 1985. Pergamon.Google Scholar
  53. R.M.C. De Keyser, G.A. Van de Velde, and F.A.G. Dumortier. A comparative study of self-adaptive long-range predictive control methods. Automatica, 24 (2): 149–163, 1988.MathSciNetMATHCrossRefGoogle Scholar
  54. P.D. Khandalekar and J.B. Riggs. Nonlinear process model based control and optimization of a model IV FCC unit. Comp. Chem. Engng,19(11):11531168, 1995.Google Scholar
  55. D. Kunii and O. Levenspiel. Fluidization Engineering. Butterworth, Stoneham, 2nd edition, 1991.Google Scholar
  56. H.I. De Lasa, A. Errazu, E. Barreiro, and S. Solioz. Analysis of fluidized bed catalytic cracking regenerator models in an industrial scale unit. Can. J. Chem. Eng, 59: 549–553, 1981.CrossRefGoogle Scholar
  57. E. Lee and F.R.Jr. Groves. Mathematical model of the fluidized bed catalytic cracking plant. Trans. Soc. Comput. Sim, 2: 219–236, 1985.Google Scholar
  58. J.H. Lee. Recent advances in model predictive control and other related areas. In Chemical Process Control-CPC V, Tahoe, California, 1996.Google Scholar
  59. J.H. Lee, M. Morari, and C.E. Garcia. State-space interpretation of model predictive control. Automatica, 30: 707–717, 1994.MathSciNetMATHCrossRefGoogle Scholar
  60. S. Li, K.Y. Lim, and D.G. Fisher. A state space formulation for model predictive control. AIChE. J, 35 (2): 241–249, 1989.CrossRefGoogle Scholar
  61. P. Lunström, J.H. Lee, M. Morari, and S. Skogestad. Limitations of dynamic matrix control. Comp. Chem. Engng, 19 (4): 409–421, 1995.CrossRefGoogle Scholar
  62. J.M. Maciejowski. Predictive Control. Pearson Education, Harlow, England, 2002.Google Scholar
  63. P. Malay. A Modified Integrated Dynamic Model of a Riser Type FCC Unit. Master’s thesis, University of Saskatchewan, Saskatoon, Canada, 1998.Google Scholar
  64. B.R. Maner, F.J. Doyle III, B.A. Ogunnaike, and R.K. Pearson. Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order Volterra models. Automatica, 32 (9): 1285–1301, 1996.MathSciNetMATHCrossRefGoogle Scholar
  65. D.Q. Mayne. Nonlinear model predictive control: an assessment. In Chemical Process Control-CPC V, Tahoe, California, 1996.Google Scholar
  66. D.Q. Mayne and H. Michalska. Receding horizon control of nonlinear systems. IEEE Trans. Automat. Contr, AC35(7): 814–824, 1990.Google Scholar
  67. D.Q. Mayne, J.B. Rawlings, C.V. Rao, and P.O.M. Scokaert. Constrained model predictive control: Stability and optimality. Automatica, 36: 789–814, 2000.MATHCrossRefGoogle Scholar
  68. R.C. McFarlane, R.C. Reinemann, J.F. Bartee, and C. Georgakis. Analysis of fluidized bed catalytic cracking regenerator models in an industrial scale unit. Comp. Chem. Engng, 17: 275–300, 1993.CrossRefGoogle Scholar
  69. H. Michalska and D.Q. Mayne. Robust receding horizon control of constrained nonlinear systems. IEEE Trans. Automat. Contr, AC38(11): 1623–1633, 1993.Google Scholar
  70. J.J. Monge and C. Georgakis. Multivariable control of catalytic cracking processes. Chem. Eng. Comm, 61: 197–225, 1987.CrossRefGoogle Scholar
  71. M. Morari and J.H. Lee. Model predictive control: the good, the bad and the ugly. In Y. Arkun and W. H. Ray, editors, Chemical Process Control–CPC IV, pages 419–444, Amsterdam, 1991. Elsevier.Google Scholar
  72. M. Morari and J.H. Lee. Model predictive control: past, present and future. Comp. Chem. Engng, 23: 667–682, 1999.CrossRefGoogle Scholar
  73. A.M. Morshedi, C.R. Cutler, and T.A. Skrovanek. Optimal solution of dynamic matrix control with linear programming techniques. pages 199–208. Proc. Am. Control. Conf., Boston, 1985.Google Scholar
  74. S.J. Qin and T.A. Badgwell. An overview of nonlinear model predictive control applications. In F. Allgöwer and A. Zheng, editors, Non Linear Model Predictive Control, pages 369–392. Birkhäuser, Basel, 2000.CrossRefGoogle Scholar
  75. S.J. Qin and T.A. Badgwell. An overview of industrial model control technology. In Chemical Process Control–CPC V, pages 232–255, Tahoe, California, 1996.Google Scholar
  76. J.B. Rawlings and K.R. Muske. The stability of constrained receding control. IEEE Transactions on Automatic Control, 38 (10): 1512–1516, 1993.MathSciNetMATHCrossRefGoogle Scholar
  77. J.B. Rawlings, E.S. Meadows, and K.R. Muske. Nonlinear model predictive control: A tutorial and survey. In Advanced Control of Chemical Processes,pages 203–224, Kyoto (Japan), 1994. IFAC.Google Scholar
  78. J. Richalet, A. Rault, J.L. Testud, and J. Papon. Model predictive heuristic control: Applications to industrial processes. Automatica, 14: 413–428, 1978.CrossRefGoogle Scholar
  79. N.L. Ricker. Model predictive control with state estimation. Ind. Eng. Chem. Res, 29: 374–382, 1990a.CrossRefGoogle Scholar
  80. N.L. Ricker. Model predictive control of processes with many inputs and outputs. Control and System Dynamics, 37: 217–269, 1990b.CrossRefGoogle Scholar
  81. N.L. Ricker. Model predictive control: State of the art. In Y. Arkun and W. H. Ray, editors, Chemical Process Control - CPC IV, Amsterdam, 1991. Elsevier.Google Scholar
  82. J.M. Martin Sanchez and J. Rodellar. Adaptive Predictive Control. Prentice Hall, Englewood Cliffs, New Jersey, 1996.Google Scholar
  83. R. Shridar and D.J. Cooper. A novel tuning strategy for multivariable model predictive control. ISA Transactions, 36 (4): 273–280, 1998.CrossRefGoogle Scholar
  84. R. Soeterboek. Predictive Control - A Unified Approach. Prentice Hall, Englewood Cliffs, New Jersey, 1992.Google Scholar
  85. J.G. Van Antwerp and R.D. Braatz. Model predictive control of large scale processes. J. Proc. Cont, 10:1-8, 2000.Google Scholar
  86. P. Vuthandam, H. Genceli, and M. Nikolaou. Performance bounds for robust quadratic dynamic matrix control with end condition. AIChE J, 41 (9): 2083–2097, 1995.CrossRefGoogle Scholar
  87. B.E. Ydstie. Extende horzion adaptive control. page 911–915, Oxford, 1984. IFAC 9th World Congress Budapest Hungary, Pergamon.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Jean-Pierre Corriou
    • 1
  1. 1.Laboratoire des Sciences du Génie Chimique, Ecole Nationale Supérieure des Industries ChimiquesLSGC-CNRS-ENSICNancy CedexFrance

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