Enhancing statistical performance of data-driven controller tuning via \(\mathcal{L}_2-\)regularization

DOI10.1016/j.automatica.2014.04.001zbMath1296.93062OpenAlexW2046654328WikidataQ58220152 ScholiaQ58220152MaRDI QIDQ458787

Alireza Karimi, Simone Formentin

Publication date: 8 October 2014

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: http://infoscience.epfl.ch/record/197159

zbMATH Keywords

convex optimizationregularizationidentification for controldata-driven controlCbTinput-output (I/O) dataoptimal feedback control lawsVRFT

Mathematics Subject Classification ID

Feedback control (93B52) Design techniques (robust design, computer-aided design, etc.) (93B51) Transformations (93B17)

Related Items (7)

Data-driven design of two degree-of-freedom nonlinear controllers: the \(\operatorname{D}^2\)-IBC approach ⋮ Direct data‐driven design of switching controllers ⋮ Model‐free adaptive control for a class of nonlinear systems with uniform quantizer ⋮ Data-driven designs of observers and controllers via solving model matching problems ⋮ Direct learning of LPV controllers from data ⋮ Direct data-driven design of LPV controllers with soft performance specifications ⋮ Deterministic continuous-time virtual reference feedback tuning (VRFT) with application to PID design

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