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

dc.contributor.authorKlauser, Elias
dc.contributor.authorGupta, Vaibhav
dc.contributor.authorKarimi, Alireza
dc.date.accessioned2023-10-30T12:54:30Z
dc.date.available2023-10-30T12:54:30Z
dc.date.issued2023-11-07
dc.description.abstractA new approach for modelling uncertainty as elliptical set for robust controller synthesis is presented in the paper. Given a set of frequency response functions of linear timeinvariant (LTI) single-input single-output (SISO) systems, the best linear nominal model and the corresponding elliptical uncertainty set, which is consistent with the data, is found. Using a novel split representation of uncertainty, this uncertainty set is converted into an equivalent integral quadratic constraint (IQC). Finally, this IQC is integrated into a data-driven frequency-domain controller synthesis method by convex optimisation. Simulation and experimental results show a 'tighter' uncertainty set and improved stability margins using the proposed method compared to the classical methods using disk uncertainty.
dc.identifier.citationIFAC World Congress 2023, Yokohama (JP)
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1277
dc.titleData-driven IQC-Based Uncertainty Modelling for Robust Control Design
dc.typeProceedings Article
dc.type.csemdivisionsDiv-E
dc.type.csemresearchareasScientific Instrumentation
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