“Improved Target Calculation for Model Predictive Control”

Authors: Morten Hovd,
Affiliation: NTNU, Department of Engineering Cybernetics
Reference: 2007, Vol 28, No 3, pp. 81-86.

Keywords: Model predictive control, target calculation, disturbances

Abstract: In industrial Model Predictive Control (MPC) applications, it is common to perform target calculation at each sample instant. The purpose of the target calculation is to translate operational targets supplied by higher level optimization functions into control targets that are feasible in the face of current disturbances. This paper shows that the commonly used target calculation formulation is flawed, and that this can lead to significant economic loss. A method for dealing with the identi¯ed problem is proposed.

PDF PDF (151 Kb)        DOI: 10.4173/mic.2007.3.3

DOI forward links to this article:
[1] Mark L. Darby, Michael Nikolaou, James Jones and Doug Nicholson (2011), doi:10.1016/j.jprocont.2011.03.009
[2] Richard Adamson, Martin Hobbs, Andy Silcock and Mark J. Willis (2017), doi:10.1016/j.compchemeng.2017.04.001
[1] Bemporad, A., Morari, M., Dua, V., Pistikopoulos, E. N. (2002). The explicit linear quadratic regulator for constrained systems, Automatica, 38:3 - 20 doi:10.1016/S0005-1098(01)00174-1
[2] Friedman, Y. (1995). What´s wrong with unit closed loop optimization, Hydrocarbon Processing,.October:107-116.
[3] Govatsmark, M. (2003). Integrated Optimization and Control, Ph.D. thesis, Norwegian University of Science and Technology, Dept. of Chemical Engineering.
[4] Kadam, J. Marquardt, W. (2004). Sensitivity-based solution updates in closed-loop dynamic optimization, In Proceedings of the DYCOPS 7 conference.
[5] Larsson, T., Govatsmark, M., Skogestad, S., Yu, C. (2003). Control structure selection for reactor, separator and recycle process, Ind. Eng. Chem. Res., 42.
[6] Marlin, T. Hrymak, A. N. (1997). Real-time operations optimization of continuous processes, AIChE Symposium Series,.93:156-164.
[7] Muske, K. R. (1997). Steady-state target optimization in linear model predictive control, In Proc. American Control Conference. pp. 3597-3601.
[8] de Prada, C. Valentin, A. (1996). Set point optimization in multivariable constrained predictive control, In Proc. 13th IFAC World Congress. San Francisco, California.
[9] Qin, S. J. Badgwell, T. A. (2003). A survey of industrial model predictive control technology, Control Engineering Practice, pp. 733-764 doi:10.1016/S0967-0661(02)00186-7
[10] Rawlings, J. B. (2000). Tutorial overview of model predictive control, IEEE Control Systems Magazine, 2.3:38-52 doi:10.1109/37.845037
[11] Rawlings, J. B. Muske, K. R. (1993). The stability of constrained receding horizon control, IEEE Transactions on Automatic Control, 3.10:1512-1516 doi:10.1109/9.241565
[12] Skogestad, S. Postlethwaite, I. (1996). Multivariable Feedback Control, Analysis and Design. John Wiley and Sons Ltd, Chichester, England.
[13] Ying, C.-M. Joseph, B. (1999). Performance and stability analysis of lp-mpc and qp-mpc cascade control systems, AIChE Journal, 4.7:1521 - 1534 doi:10.1002/aic.690450714

  title={{Improved Target Calculation for Model Predictive Control}},
  author={Hovd, Morten},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}