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Feedback min‐max model predictive control using a single linear program: robust stability and the explicit solution

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Kerrigan, Eric C., Maciejowski, Jan M.·2004·International Journal of Robust and Nonlinear Control

Abstract

In this paper we introduce a new stage cost and show that the use of this cost allows one to formulate a robustly stable feedback min–max model predictive control problem that can be solved using a single linear program. Furthermore, this is a multi‐parametric linear program, which implies that the optimal control law is piecewise affine and can be explicitly pre‐computed so that the linear program does not have to be solved on‐line. We assume that the plant model is known, is discrete‐time and linear time‐invariant, is subject to unknown but bounded state disturbances and that the states of the system are measured. Two numerical examples are presented; one of these is taken from the literature, so that a direct comparison of solutions and computational complexity with earlier proposals is possible. Copyright © 2004 John Wiley & Sons, Ltd.

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Metadata

Authors
Kerrigan, E. C., & Maciejowski, J. M.
Venue
International Journal of Robust and Nonlinear Control
Publisher
Wiley
Published
2004-01-29
Volume
14
Issue
4
Pages
395-413
ISSN
  • 1049-8923 (ISSN-L)
  • 1099-1239
OpenAlex
W2149276643
Cited by
222
References
1
FWCI
36.40
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Feedback min‐max model predictive control using a single linear program: robust stability and the explicit solution. https://app.cochat.ai/papers/10.1002/rnc.889
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Kerrigan, E. C., & Maciejowski, J. M. (2004). Feedback min‐max model predictive control using a single linear program: robust stability and the explicit solution. International Journal of Robust and Nonlinear Control. https://doi.org/10.1002/rnc.889

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.