Enhancing Output-Feedback MPC With Set-Valued Moving Horizon Estimation

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OriginalspracheEnglisch
Aufsatznummer8253530
Seiten (von - bis)2976-2986
Seitenumfang11
FachzeitschriftIEEE Transactions on Automatic Control
Jahrgang63
Ausgabenummer9
PublikationsstatusVeröffentlicht - 1 Sept. 2018
Extern publiziertJa

Abstract

We consider output feedback control of linear discrete-time systems subject to bounded additive disturbances and measurement noise. The goal is to stabilize the system while ensuring the satisfaction of hard constraints on the state and input. For this purpose, we present a novel output-feedback model predictive control (MPC) scheme based on set-valued estimation. The main feature of the scheme is that the number of past measurements used in order to obtain the set-valued estimate depends on the particular time step in the prediction horizon for which the estimate is required. In particular, we employ fewer measurements for prediction steps that are farther in the future, which is a key point in establishing recursive feasibility. The resulting optimal control problem is of bounded complexity, which is a priori known, and is a linearly constrained convex optimization problem under additional assumptions. We demonstrate in a numerical example that the proposed MPC scheme allows an enlargement of the feasible set beyond what is possible with earlier schemes using only linear estimators.

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Enhancing Output-Feedback MPC With Set-Valued Moving Horizon Estimation. / Brunner, Florian D.; Muller, Matthias A.; Allgower, Frank.
in: IEEE Transactions on Automatic Control, Jahrgang 63, Nr. 9, 8253530, 01.09.2018, S. 2976-2986.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Brunner FD, Muller MA, Allgower F. Enhancing Output-Feedback MPC With Set-Valued Moving Horizon Estimation. IEEE Transactions on Automatic Control. 2018 Sep 1;63(9):2976-2986. 8253530. doi: 10.1109/TAC.2018.2791899
Brunner, Florian D. ; Muller, Matthias A. ; Allgower, Frank. / Enhancing Output-Feedback MPC With Set-Valued Moving Horizon Estimation. in: IEEE Transactions on Automatic Control. 2018 ; Jahrgang 63, Nr. 9. S. 2976-2986.
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N1 - Funding information: Manuscript received January 18, 2017; revised January 20, 2017 and July 8, 2017; accepted October 31, 2017. Date of publication January 10, 2018; date of current version August 28, 2018. This work was supported by German Research Foundation (DFG) for the project within the Cluster of Excellence in Simulation Technology (EXC 310/2) at the University of Stuttgart, Stuttgart, Germany. Recommended by Associate Editor Ilya V. Kolmanovsky. (Corresponding author: Florian David Brunner.) The authors are with the Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart 70569, Germany (e-mail: fdbrunner@gmail.com; mueller@ist.uni-stuttgart.de; allgower@ist.uni-stuttgart.de).

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N2 - We consider output feedback control of linear discrete-time systems subject to bounded additive disturbances and measurement noise. The goal is to stabilize the system while ensuring the satisfaction of hard constraints on the state and input. For this purpose, we present a novel output-feedback model predictive control (MPC) scheme based on set-valued estimation. The main feature of the scheme is that the number of past measurements used in order to obtain the set-valued estimate depends on the particular time step in the prediction horizon for which the estimate is required. In particular, we employ fewer measurements for prediction steps that are farther in the future, which is a key point in establishing recursive feasibility. The resulting optimal control problem is of bounded complexity, which is a priori known, and is a linearly constrained convex optimization problem under additional assumptions. We demonstrate in a numerical example that the proposed MPC scheme allows an enlargement of the feasible set beyond what is possible with earlier schemes using only linear estimators.

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