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  4. Data-driven IQC-Based Uncertainty Modelling for Robust Control Design
 
conference paper

Data-driven IQC-Based Uncertainty Modelling for Robust Control Design

Gupta, Vaibhav  
•
Klauser, Elias Sebastian  
•
Karimi, Alireza  
2023
IFAC World Congress 2023

A new approach is presented in the paper for modelling uncertainty as an elliptical set for robust controller synthesis. The method involves finding the best linear nominal model and the corresponding elliptical uncertainty set that is consistent with a set of frequency response functions of linear time-invariant (LTI) single-input single-output (SISO) systems. The uncertainty set is then converted into an equivalent integral quadratic constraint (IQC) using a novel split representation of uncertainty. Finally, the IQC is integrated into a data-driven frequency-domain controller synthesis method through convex optimization. The simulation and experimental results demonstrate that the proposed method yields a “tighter” uncertainty set and improved stability margins compared to classical methods that use disk uncertainty.

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Type
conference paper
DOI
10.1016/j.ifacol.2023.10.1244
Author(s)
Gupta, Vaibhav  

EPFL

Klauser, Elias Sebastian  

EPFL

Karimi, Alireza  

EPFL

Date Issued

2023

Published in
IFAC-PapersOnLine
Volume

56

Issue

2

Start page

4789

End page

4795

Subjects

Data-based control

•

Robust control (linear case)

•

Uncertainty descriptions

•

Convex optimization

•

Frequency-domain identification for control

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
SCI-STI-AK  
Event nameEvent acronymEvent placeEvent date
IFAC World Congress 2023

Yokohama, Japan

2023-07-09 - 2023-07-14

FunderFunding(s)Grant NumberGrant URL

Swiss National Science Foundation

200021-204962

European Space Agency

4000133258/20/NL/MH/hm

RelationRelated workURL/DOI

IsSupplementedBy

[Code] Elliptical uncertainty modelling - SISO

https://gitlab.epfl.ch/ddmac/paper-codes/2023/elliptical-uncertainty-modelling-siso/-/tree/v1.0.0?ref_type=tags
Available on Infoscience
October 9, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/196623.3
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