Computationally efficient robust MPC using optimized constraint tightening
Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
Data
Rights / License
Abstract
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline design of a disturbance-affine feedback gain whereby the resulting constraint tightening is minimized. This is achieved by formulating the constraint tightening problem as a convex optimization problem with the feedback term as a variable. The resulting MPC controller has the computational complexity of nominal MPC, and guarantees recursive feasibility, stability and constraint satisfaction. The advantages of the proposed approach compared to existing robust MPC methods are demonstrated using numerical examples.
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Publication status
published
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Editor
Book title
2022 IEEE 61st Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
1770 - 1775
Publisher
IEEE
Event
61st IEEE Conference on Decision and Control (CDC 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
Notes
Conference lecture held on December 6, 2022
Funding
178890 - Modeling, Identification and Control of Periodic Systems in Energy Applications (SNF)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000537515