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“Model-Based Optimizing Control and Estimation Using Modelica Model”

Authors: Lars Imsland, Pål Kittilsen and Tor S. Schei,
Affiliation: NTNU, Department of Engineering Cybernetics
Reference: 2010, Vol 31, No 3, pp. 107-121.

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Keywords: Non-linear model predictive control, state estimation, Modelica, offshore oil- and gas production, gradient computation

Abstract: This paper reports on experiences from case studies in using Modelica/Dymola models interfaced to control and optimization software, as process models in real time process control applications. Possible applications of the integrated models are in state- and parameter estimation and nonlinear model predictive control. It was found that this approach is clearly possible, providing many advantages over modeling in low-level programming languages. However, some effort is required in making the Modelica models accessible to NMPC software.

PDF PDF (367 Kb)        DOI: 10.4173/mic.2010.3.3



DOI forward links to this article:
  [1] Manuel Gr ber, Christian Kirches, Dirk Scharff and Wilhelm Tegethoff (2012), doi:10.3384/ecp12076781
  [2] Wenbin Jiang, Shuguang Wang, Hai Jin and Yong Huang (2012), doi:10.1109/APSCC.2012.70
  [3] L. Vanfretti, W. Li, T. Bogodorova and P. Panciatici (2013), doi:10.1109/PESMG.2013.6672476
  [4] Andrés Codas, Marco Aurélio S. Aguiar, Konstantin Nalum and Bjarne Foss (2013), doi:10.3182/20130904-3-FR-2041.00069
  [5] Gang Cao, Edmund M-K Lai and Fakhrul Alam (2016), doi:10.1109/AMC.2016.7496359
  [6] Gang Cao, Edmund M-K Lai and Fakhrul Alam (2016), doi:10.1109/ICCAR.2016.7486726


References:
[1] Biegler, L. (2000). Efficient solution of dynamic optimization and NMPC problems, In F. Allgöwer and A. Zheng, editors, Nonlinear Predictive Control, pages 219-245. Birkhauser, Basel.
[2] Biegler, L. T., Cervantes, A. M., Wächter, A. (2002). Advances in simultaneous strategies for dynamic process optimization, Chem. Eng. Sci., 57:575-593 doi:10.1016/S0009-2509(01)00376-1
[3] Bock, H. G., Diehl, M., Leineweber, D. B., Schlöder, J. P. (2000). A direct multiple shooting method for real-time optimization of nonlinear DAE processes, In F. Allgöwer and A. Zheng, editors, Nonlinear Predictive Control, volume 26 of Progress in Systems Theory, pages 246-267. Birkhäuser, Basel.
[4] Findeisen, R., Imsland, L., Allgöwer, F., Foss, B. A. (2003). State and output feedback nonlinear model predictive control: An overview, European J. of Control, .2-3:190-206 doi:10.3166/ejc.9.190-206
[5] Foss, B. A. Schei, T. S. (2007). Putting nonlinear model predictive control into use, In Assessment and Future Directions Nonlinear Model Predictive Control, LNCIS 358, pages 407-417. Springer Verlag.
[6] Hairer, E., Nørsett, S. P., Wanner, G. (1993). Solving Ordinary Differential Equations I - Nonstiff problems, Springer-Verlag, 2nd edition.
[7] Hindmarsh, A. C. Serban, R. (2006). User Documentation for CVODES v2,5,0, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory.
[8] Imsland, L., Kittilsen, P., Schei, T. S. (2008). Modelbased optimizing control and estimation using modelica models, In Proc. of Modelica´2008. Bielefeld, Germany.
[9] Imsland, L., Kittilsen, P., Schei, T. S. (2009). Using modelica models in real time dynamic optimization - gradient computation, In Proc. of Modelica´2009. Como, Italy.
[10] Jørgensen, J. B. (2007). Adjoint sensitivity results for predictive control, state- and parameter-estimation with nonlinear models, In Proceedings of the European Control Conference, Kos, Greece.
[11] Lang, Y. D. Biegler, L. T. (2007). A software environment for simultaneous dynamic optimization, Computers and& Chemical Engineering, 3.8:931-942 doi:10.1016/j.compchemeng.2006.10.017
[12] Li, W. C. Biegler, L. T. (1989). Multistep, Newtontype control strategies for constrained, nonlinear processes, Chem. Eng. Res. Des., 67:562-577.
[13] Maciejowski, J. M. (2001). Predictive Control with Constraints, Prentice-Hall.
[14] Nocedal, J. Wright, S. J. (2006). Numerical Optimization, Springer-Verlag, New York.
[15] Nørgaard, M., Poulsen, N. K., Ravn, O. (2000). New developments in state estimation for nonlinear systems, Automatica, 36(11):1627-1638 doi:10.1016/S0005-1098(00)00089-3
[16] Qin, S. J. Badgwell, T. A. (2003). A survey of industrial model predictive control technology, Control Engineering Practice, 11:733-764 doi:10.1016/S0967-0661(02)00186-7
[17] Rawlings, J. B. Mayne, D. Q. (2009). Model Predictive Control: Theory and Design, Nob Hill Publishing, Madison, WI, 576 pages, ISBN 978-0-9759377-0-9.
[18] Ringset, R., Imsland, L., Foss, B. A. (2010). On gradient computation in single-shooting nonlinear model predictive control, In Proc. of IFAC DYCOPS 2010, Leuven, Belgium.
[19] Schei, T. S. (1997). A finite-difference method for linearization in nonlinear estimation algorithms, Automatica, 33(11):2053-2058 doi:10.1016/S0005-1098(97)00127-1
[20] Schei, T. S. (2007). On-line estimation for process control and optimization applications, In Proc. 8th International IFAC Symposium on Dynamics and Control of Process Systems.DYCOPS-07. Cancún, Mexico.
[21] Schei, T. S. (2008). On-line estimation for process control and optimization applications, Journal of Process Control, 18:821-828 doi:10.1016/j.jprocont.2008.06.014
[22] Schlegel, M., Marquardt, W., Ehrig, R., Nowak, U. (2004). Sensitivity analysis of linearly-implicit differentialalgebraic systems by one-step extrapolation, Applied Numerical Mathematics, 48:83-102.


BibTeX:
@article{MIC-2010-3-3,
  title={{Model-Based Optimizing Control and Estimation Using Modelica Model}},
  author={Imsland, Lars and Kittilsen, Pål and Schei, Tor S.},
  journal={Modeling, Identification and Control},
  volume={31},
  number={3},
  pages={107--121},
  year={2010},
  doi={10.4173/mic.2010.3.3},
  publisher={Norwegian Society of Automatic Control}
};

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