Details
| Original language | English |
|---|---|
| Article number | 8253530 |
| Pages (from-to) | 2976-2986 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Automatic Control |
| Volume | 63 |
| Issue number | 9 |
| Publication status | Published - 1 Sept 2018 |
| Externally published | Yes |
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.
Keywords
- Estimation, linear systems, predictive control for linear systems, robust control
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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In: IEEE Transactions on Automatic Control, Vol. 63, No. 9, 8253530, 01.09.2018, p. 2976-2986.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Enhancing Output-Feedback MPC With Set-Valued Moving Horizon Estimation
AU - Brunner, Florian D.
AU - Muller, Matthias A.
AU - Allgower, Frank
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).
PY - 2018/9/1
Y1 - 2018/9/1
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.
AB - 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.
KW - Estimation
KW - linear systems
KW - predictive control for linear systems
KW - robust control
UR - http://www.scopus.com/inward/record.url?scp=85043763742&partnerID=8YFLogxK
U2 - 10.1109/TAC.2018.2791899
DO - 10.1109/TAC.2018.2791899
M3 - Article
VL - 63
SP - 2976
EP - 2986
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
SN - 0018-9286
IS - 9
M1 - 8253530
ER -