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“Interaction between control and estimation in nonlinear MPC”

Authors: Morten Hovd and Robert R. Bitmead,
Affiliation: NTNU, Department of Engineering Cybernetics and University of California San Diego
Reference: 2005, Vol 26, No 3, pp. 165-174.

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Keywords: Nonlinear MPC, state estimation

Abstract: This paper shows how to take the quality of the state estimation into account in the formulation of the optimization criterion for model predictive control (MPC). This is relevant for the control of nonlinear plants, for which the separation principle in general does not apply. The method is illustrated on an example which is locally weakly unobservable at the reference state.

PDF PDF (165 Kb)        DOI: 10.4173/mic.2005.3.4

DOI forward links to this article:
  [1] Boris Houska, Dries Telen, Filip Logist and Jan Van Impe (2017), doi:10.1137/15M1049865
  [2] Xuhui Feng and Boris Houska (2017), doi:10.1016/j.jprocont.2017.10.003

[1] Adetola, V. M. Guay (2003). Nonlinear output feedback receding horizon control of sampled data systems, In: Proc. of the American Control Conference. Denver, Colorado. pp. 4914-4919.
[2] Allg÷wer, F., T. A. Badgewell, J. S. Qin, J. B. Rawlings S. J. Wright (1999). Nonlinear model predictive control and moving horizon estimation - an introductory overview, In: Advances in Control. Highlights of the ECC┤99. Springer.
[3] Biegler, L. T. (1998). Advances in nonlinear programming concepts for process control, Journal of Process Control 8, 301-311 doi:10.1016/S0959-1524(98)00009-2
[4] Imsland, L., R. Findeisen, E. Bullinger, F. Allg÷wer B.A. Foss (2003). A note on stability, robustness and performance of output feedback nonlinear model predictive control, Journal of Process Control 13, 633-644 doi:10.1016/S0959-1524(03)00006-4
[5] Johansen, T. A. (2002). On multi-parametric nonlinear programming and explicit nonlinear model predictive control, In: IEEE Conf. Decision and Control. Las Vegas, NV, USA. pp. 2768-2773.
[6] Qin, S. J. T. A. Badgwell (2003). A survey of industrial model predictive control technology, Control Engineering Practice pp. 733-764 doi:10.1016/S0967-0661(02)00186-7
[7] Rao, C. V. (2000). Moving Horizon Strategies for the Constrained Monitoring and Control of Nonlinear Discrete-Time Systems, PhD thesis. Department of Chemical Engineering, University of Wisconsin - Madison.
[8] Shouche, M. S., H. Genceli M. Nikolaou (2002). Effect of on-line optimization techniques on model predictive control and identification ┤MPCI┤, Computers and Chemical Engineering 26, 1241-1252.
[9] Yan, J. R. R. Bitmead (2002). Model predictive control and state estimation: A network example, In: IFAC World Congress. Barcelona, Spain doi:10.1016/S0098-1354(02)00091-1

  title={{Interaction between control and estimation in nonlinear MPC}},
  author={Hovd, Morten and Bitmead, Robert R.},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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