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 -