\(H_\infty\)-norm-based optimization for the identification of gray-box LTI state-space model parameters

DOI10.1016/j.sysconle.2016.03.003zbMath1338.93123OpenAlexW2319977202MaRDI QIDQ286511

Daniel Vizer, Olivier Prot, Guillaume Mercère, Edouard Laroche

Publication date: 20 May 2016

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sysconle.2016.03.003

zbMATH Keywords

system identificationparameterizationnon-smooth optimization\(H_\infty\)-normgray-box model

Mathematics Subject Classification ID

Related Items (4)

Identification of structured state-space models ⋮ Robust time-domain output error method for identifying continuous-time systems with time delay ⋮ Large-Scale Estimation of Dominant Poles of a Transfer Function by an Interpolatory Framework ⋮ A Subspace Framework for ${\mathcal H}_\infty$-Norm Minimization

Cites Work

• Kernel methods in system identification, machine learning and function estimation: a survey
• Direct continuous-time approaches to system identification. Overview and benefits for practical applications
• An estimator of the inverse covariance matrix and its application to ML parameter estimation in dynamical systems
• Identification of Parameterized Gray-Box State-Space Systems: From a Black-Box Linear Time-Invariant Representation to a Structured One
• Filtering and System Identification
• A Trust Region Spectral Bundle Method for Nonconvex Eigenvalue Optimization
• On Gradient-Based Search for Multivariable System Estimates
• Nonsmooth H_∞Synthesis
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