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

dc.contributor.authorKlauser, Elias
dc.contributor.authorGupta, Vaibhav
dc.contributor.authorKarimi, Alireza
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.titleData-driven IQC-Based Uncertainty Modelling for Robust Control Design
dc.typeProceedings Article
dc.type.csemresearchareasScientific Instrumentation