Direct identification of continuous-time LPV state-space models via an integral architecture

DOI10.1016/j.automatica.2022.110407zbMath1496.93038OpenAlexW4281556934WikidataQ114204779 ScholiaQ114204779MaRDI QIDQ2151932

Bojan Mavkov, Marco Forgione, Manas Mejari, Dario Piga

Publication date: 5 July 2022

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2022.110407

zbMATH Keywords

tokamak plasmastate-space identificationlinear parameter-varying modelscontinuous-time identification

Mathematics Subject Classification ID

System identification (93B30) Linear systems in control theory (93C05)

Uses Software

• Matlab
• System Identification Toolbox

Cites Work

• Unnamed Item
• Continuous-time identification of periodically parameter-varying state space models
• Direct continuous-time approaches to system identification. Overview and benefits for practical applications
• A bias-correction method for closed-loop identification of linear parameter-varying systems
• Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification
• LPV system identification under noise corrupted scheduling and output signal observations
• Kernel methods for subspace identification of multivariable LPV and bilinear systems
• Linear parameter-varying subspace identification: a unified framework
• Identification of linear parameter varying models
• Subspace identification of continuous-time state-space LPV models
• Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators
• The advantages of directly identifying continuous-time transfer function models in practical applications
• Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming

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