Supervisory predictive control and on-line set-point optimization

Piotr Tatjewski

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 3, page 483-495
  • ISSN: 1641-876X

Abstract

top
The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.

How to cite

top

Piotr Tatjewski. "Supervisory predictive control and on-line set-point optimization." International Journal of Applied Mathematics and Computer Science 20.3 (2010): 483-495. <http://eudml.org/doc/208001>.

@article{PiotrTatjewski2010,
abstract = {The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.},
author = {Piotr Tatjewski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {predictive control; nonlinear control; linearization; model uncertainty; constrained control; set-point optimization},
language = {eng},
number = {3},
pages = {483-495},
title = {Supervisory predictive control and on-line set-point optimization},
url = {http://eudml.org/doc/208001},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Piotr Tatjewski
TI - Supervisory predictive control and on-line set-point optimization
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 3
SP - 483
EP - 495
AB - The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
LA - eng
KW - predictive control; nonlinear control; linearization; model uncertainty; constrained control; set-point optimization
UR - http://eudml.org/doc/208001
ER -

References

top
  1. Allgöwer, F., Badgwell, T., Qin, J., Rawlings, J. and Wright, S. (1999). Nonlinear predictive control and moving horizon estimation-An introductory overview, in P. Frank (Ed.), Advances in Control-Highlights of ECC'99, Springer, London, Chapter 12. 
  2. Bemporad, A. (1998). Reference governor for constrained nonlinear systems, IEEE Transactions on Automatic Control 43(3): 415-419. Fig. 10. Trajectories of the control signal in the multilayer structure as in Fig. 9. Zbl0906.93024
  3. Bemporad, A. and Morari, M. (1999). Robust model predictive control: A survey, in A. Garulli, A. Tesi and A. Vicino (Eds.), Robustness in Identification and Control, Lecture Notes in Control and Information Sciences, Vol. 245, Springer, New York, NY. Zbl0979.93518
  4. Bemporad, A., Morari, M., Dua, V. and Pistikopoulos, E. (2002). The explicit linear-quadratic regulator for constrained systems, Automatica 38(1): 3-20. Zbl0999.93018
  5. Blevins, T.L., McMillan, G.K., Wojsznis, W.K. and Brown, M.W. (2003). Advanced Control Unleashed, ISA Society, Research Triangle Park, NC. 
  6. Brdys, M.A. and Chang, T. (2001). Robust model predictive control of chlorine residuals in water systems based on a state space modeling, in B. Ulanicki, B. Coulbeck and J. Rance (Eds.), Water Software Systems: Theory and Applications, Research Studies Press, Baldock. 
  7. Brdys, M. and Tatjewski, P. (2005). Iterative Algorithms for Multilayer Optimizing Control, Imperial College Press/World Scientific, London/Singapore. Zbl1083.93001
  8. Camacho, E. and Bordons, C. (1999). Model Predictive Control, Springer, London. 
  9. Deinrych, P., Szumski, A., Brdys, M.A., Grochowski, M. and Szczygielski, P. (2007). Softly switched robustly feasible mpc for constrained linear systems under set bounded uncertainty, Proceedings of the 11th IFAC/IFORS/IMACS/IFIP Symposium on Large Scale Complex Systems Theory and Applications, Gdańsk, Poland, (on CD-ROM). 
  10. Diehl, M. and Bjornberg, J. (2004). Robust dynamic programming for minmax model predictive control of constrained uncertain systems, IEEE Transactions on Automatic Control 49(12): 2253-2257. 
  11. DoyleIII, F.J., Ogunnaike, B.A. and Pearson, R.K. (1995). Nonlinear model-based control using second-order Volterra models, Automatica 31(5): 669-714. Zbl0823.93022
  12. Feng, G. and Lozano, R. (1999). Adaptive Control Systems, Newness, Oxford. 
  13. Findeisen, W. (1997). Control Structures for Complex Processes, Warsaw University of Technology Press, Warsaw, (in Polish). 
  14. Findeisen, W., Bailey, F.N., Brdyś, M., Malinowski, K., Tatjewski, P. and Woźniak, A. (1980). Control and Coordination in Hierarchical Systems, J. Wiley & Sons, Chichester/New York, NY/Brisbane/Toronto. Zbl0534.93002
  15. Goodwin, G.C., Graebe, S.F. and Salgado, M.E. (2001). Control System Design, Prentice Hall, Upper Saddle River, NJ. 
  16. Haseltine, E.L. and Rawlings, J.B. (2005). Critical evaluation of extended Kalman filtering and moving horizon estimation, Industrial and Engineering Chemistry Research 44(5): 2451-2460. 
  17. Jeong, S.C. and Park, P. (2002). Output-feedback robust adaptive receding horizon controller design, Proceedings of the American Control Conference 2002, Anchorage, AK, USA. 
  18. Kassmann, D.E., Badgwell, T. and Hawkins, R. (2000). Robust steady-state target calculation for model predictive control, AIChE Journal 46(5): 1007-1024. 
  19. Kerrigan, E. and Maciejowski, J. (2004). Feedback minmax model predictive control using a single linear program: Robust stability and the explicit solution, International Journal of Robust and Nonlinear Control 14(4): 395-413. Zbl1051.93034
  20. Ławryńczuk, M., Marusak, P. and Tatjewski, P. (2007). Multilevel and integrated structures for predictive control and economic optimisation, Proceedings of the 11th IFAC/IFORS/IMACS/IFIP Symposium on Large Scale Complex Systems Theory and Applications, Gdańsk, Poland, (on CD-ROM). Zbl1153.93411
  21. Ławryńczuk, M., Marusak, P. and Tatjewski, P. (2008). Cooperation of model predictive control with steady-state economic optimisation, Control and Cybernetics 37(1): 133-158. Zbl1153.93411
  22. Ławryńczuk, M., Marusak, P. and Tatjewski, P. (2009a). Integrated predictive optimizer and constraint supervisor for processes with basic feedback control, Proceedings of the European Control Conference 2009, Budapest, Hungary, pp. 3359-3364. 
  23. Ławryńczuk, M., Marusak, P. and Tatjewski, P. (2009b). On cooperation of set-point optimisation and predictive control based on hammerstein models, Proceedings of the 7th Workshop on Advanced Control and Diagnosis ACD2009, Zielona Góra, Poland, (on CD-ROM). Zbl1153.93411
  24. Ławryńczuk, M. and Tatjewski, P. (2010). Nonlinear predictive control based on neural multi-models, International Journal of Applied Mathematics and Computer Science 20(1): 7-21, DOI: 10.2478/v10006-010-001-y. Zbl1300.93069
  25. Lee, J. and Yu, Z. (1997). Worst-case formulations of model predictive control for systems with bounded parameters, Automatica 33(5): 763-781. Zbl0878.93025
  26. Lefkowitz, I. (1966). Multilevel approach applied to control system design, Journal of Basic Engineering, Transactions of ASME 88(Ser. B2): 392-398. 
  27. Maciejowski, J. (2002). Predictive Control, Prentice Hall, Harlow. Zbl0978.93002
  28. Marlin, T.E. (1995). Process Control, McGraw-Hill, New York, NY. 
  29. Marusak, P. and Tatjewski, P. (2008). Actuator fault tolerance in control systems with predictive constrained set-point optimizers, International Journal of Applied Mathematics and Computer Science 18(4): 539-551, DOI: 10.2478/v10006008-0047-2. Zbl1155.93357
  30. Marusak, P. and Tatjewski, P. (2009). Effective dual-mode fuzzy dmc algorithms with on-line quadratic optimization and guaranteed stability, International Journal of Applied Mathematics and Computer Science 19(1): 127-141, DOI: 10.2478/v10006/009-0012-8. Zbl1169.93358
  31. Mayne, D., Rawlings, J., Rao, C. and Scokaert, P. (2000). Constrained model predictive control: Stability and optimality, Automatica 36(6): 789-814. Zbl0949.93003
  32. Mesarović, M.D., Macko, D. and Takahara, Y. (1970). Theory of Hierarchical, Multilevel Systems, Academic Press, New York, NY. Zbl0206.14501
  33. Qin, S. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice 11(7): 733-764. 
  34. Rao, V., Rawlings, J.B. and Mayne, D.Q. (2003). Constrained state estimation for nonlinear discrete-time systems: Stability and moving horiozon approximations, IEEE Transactions on Automatic Control 48(2): 246-258. 
  35. Roberts, P. (1979). An algorithm for steady-state optimization and parameter estimation, International Journal of Systems Science 10: 719-734. Zbl0406.93024
  36. Rossiter, J. (2003). Model-Based Predictive Control, CRC Press, Boca Raton, FL/London/New York, NY/Washington, DC. 
  37. Saez, D., Cipriano, A. and Ordys, A. W. (2002). Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants, Springer, London. 
  38. Scokaert, P. and Mayne, D. (1998). Minmax feedback model predictive control for constrained linear systems, IEEE Transactions on Automatic Control 43(8): 1136-1142. Zbl0957.93034
  39. Skogestad, S. (2000). Plantwide control: the search for the selfoptimizing control structure, Journal of Process Control 10(5): 487-507. 
  40. Skogestad, S. (2004). Control structure design for complex chemical plants, Computers and Chemical Engineering 28(1-2): 219-234. 
  41. Sztyber, K. (2008). Predictive Control Algorithms under Uncertainty, Ph.D. thesis, Warsaw University of Technology, Warsaw, (in Polish). 
  42. Tatjewski, P. (2007). Advanced Control of Industrial Processes, Springer, London. Zbl1134.93037
  43. Tatjewski, P. (2008). Advanced control and on-line process optimization in multilayer structures, Annual Reviews in Control 32(1): 71-85. 
  44. Tatjewski, P. and Ławryńczuk, M. (2006). Soft computing in model-based predictive control, International Journal of Applied Mathematics and Computer Science 16(1): 7-26. Zbl1334.93068
  45. Tatjewski, P., Ławryńczuk, M. and Marusak, P. (2006). Linking nonlinear steady-state and target set-point optimization for model predictive control, Proceedings of the Control Conference 2006, Glasgow, UK, (on CD-ROM). Zbl1153.93411
  46. Tondell, P., Johansen, T.A. and Bemporad, A. (2003). An algotithm for multiparametric quadratic programming and explicit mpc solutions, Automatica 39(3): 489-497. Zbl1019.93019
  47. Tran, V.N. and Brdys, M.A. (2009). Optimizing control by robustly feasible model predictive control and application to drinking water distribution systems, Artificial Neural Networks ICANN 2009, Lecture Notes in Computer Science, Vol. 5769, Springer, Berlin/Heidelberg. 
  48. Wang, Y. (2002). Robust Model Predictive Control, Ph.D. thesis, University of Wisconsin-Madison, Madison, WI. 
  49. Zanin, A., de Gouvea, M.T. and Odloak, D. (2002). Integrating real-time optimization into model predictive controller of the FCC system, Control Engineering Practice 10(8): 819-831. 
  50. Zheng, A., Mahajanam, R.V. and Douglas, J.M. (1999). Hierarchical procedure for plantwide control system synthesis, AIChE Journal 45(6): 1255-1265. 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.