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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 912603, 20 pages
http://dx.doi.org/10.1155/2012/912603
Research Article

Towards Online Model Predictive Control on a Programmable Logic Controller: Practical Considerations

1Department of Industrial Engineering, KAHO Sint-Lieven, Gebroeders De Smetstraat 1, 9000 Gent, Belgium
2BioTeC, Department of Chemical Engineering (CIT), KU Leuven, W. de Croylaan 46, 3001 Leuven, Belgium
3SCD, Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium

Received 10 July 2012; Revised 1 October 2012; Accepted 1 October 2012

Academic Editor: Wei-Chiang Hong

Copyright © 2012 Bart Huyck et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. J. Qin and T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Engineering Practice, vol. 11, no. 7, pp. 733–764, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. U. Mathur, R. D. Rounding, D. R. Webb, and R. J. Conroy, “Use model-predictive control to improve distillation operations,” Chemical Engineering Progress, vol. 104, no. 1, pp. 35–41, 2008. View at Scopus
  3. J. Maciejowski, Predictive Control with Constraints, Pearson Education Limited, 2002.
  4. Y. Wang and S. Boyd, “Fast model predictive control using online optimization,” IEEE Transactions on Control Systems Technology, vol. 18, no. 2, pp. 267–278, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Van den Broeck, M. Diehl, and J. Swevers, “A model predictive control approach for time optimal point-to-point motion control,” Mechatronics, vol. 21, no. 7, pp. 1203–1212, 2011.
  6. N. Giorgetti, G. Ripaccioli, A. Bemporad, I. V. Kolmanovsky, and D. Hrovat, “Hybrid model predictive control of direct injection stratified charge engines,” IEEE/ASME Transactions on Mechatronics, vol. 11, no. 5, pp. 499–506, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Bemporad, M. Morari, V. Dua, and E. N. Pistikopoulos, “The explicit linear quadratic regulator for constrained systems,” Automatica, vol. 38, no. 1, pp. 3–20, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  8. P. Tøndel, T. A. Johansen, and A. Bemporad, “An algorithm for multi-parametric quadratic programming and explicit MPC solutions,” Automatica, vol. 39, no. 3, pp. 489–497, 2003. View at Publisher · View at Google Scholar
  9. G. Pannocchia, J. B. Rawlings, and S. J. Wright, “Fast, large-scale model predictive control by partial enumeration,” Automatica, vol. 43, no. 5, pp. 852–860, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  10. J. R. Wright, “The debate over which PLC programming language is the state-of-the-art,” Journal of Industrial Technology, vol. 15, no. 4, pp. 2–5, 2006.
  11. G. Valencia-Palomo and J. A. Rossiter, “Efficient suboptimal parametric solutions to predictive control for PLC applications,” Control Engineering Practice, vol. 19, no. 7, pp. 732–743, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Kvasnica, I. Rauová, and M. Fikar, “Automatic code generation for real-time implementation of model predictive control,” in Proceedings of IEEE International Symposium on Computer-Aided Control System Design (CACSD '10), pp. 993–998, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Hildreth, “A quadratic programming procedure,” Naval Research Logistics Quarterly, vol. 4, pp. 79–85, 1957. View at Publisher · View at Google Scholar
  14. H. J. Ferreau, H. G. Bock, and M. Diehl, “An online active set strategy to overcome the limitations of explicit MPC,” International Journal of Robust and Nonlinear Control, vol. 18, no. 8, pp. 816–830, 2008. View at Publisher · View at Google Scholar
  15. T. Soderstrom and P. Stoica, System Identification, Prentice-Hall, London, UK, 1989.
  16. P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Kluwer Academic, 1996.
  17. L. Ljung, System Identification: Theory for the User, Prentice Hall, Upper Saddle River, NJ, USA, 2nd edition, 1999.
  18. C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms, Prentice-Hall, 1998.
  19. J. A. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.
  20. E. F. Camacho and C. Bordons, Model Predictive Control, Springer, 2003.
  21. L. Wang, Model Predictive Control System Design and Implementation Using MATLAB, Springer, London, UK, 2009.
  22. J. Mattingley and S. Boyd, “CVXGEN: a code generator for embedded convex optimization,” Optimization and Engineering, vol. 13, no. 1, pp. 1–27, 2012. View at Publisher · View at Google Scholar
  23. D. G. Luenberger, Optimization by Vector Space Methods, John Wiley & Sons, New York, NY, USA, 1969.
  24. L. Ljung, System Identification Toolbox Users Guide, The MathWorks, Natick, Mass, USA, 2009.
  25. B. Huyck, F. Logist, J. De Brabanter, J. Van Impe, and B. De Moor, “Constrained model predictive control on a programmable automation system exploiting code generation: practical considerations,” in Proceedings of the 18th World Congress of the International Federation of Automatic Control, pp. 12207–12212, Milano, Italy, 2011.
  26. A. N. Iusem and A. R. De Pierro, “On the convergence properties of Hildreth's quadratic programming algorithm,” Mathematical Programming, vol. 47, no. 1, pp. 37–51, 1990. View at Publisher · View at Google Scholar · View at Zentralblatt MATH