Non-parametric methods for \({\mathcal L}_2\)-gain estimation using iterative experiments

DOI10.1016/J.AUTOMATICA.2010.05.012zbMath1204.93036OpenAlexW2109831197MaRDI QIDQ608451

Håkan Hjalmarsson, B. Wahlberg, Märta Barenthin Syberg

Publication date: 25 November 2010

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2010.05.012

zbMATH Keywords

Lanczos methoditerative methodspower method\(H_\infty\) normsmall gain theoremstochastic gradient method\({\mathcal L}_2\)-gain estimation

Mathematics Subject Classification ID

Stochastic programming (90C15) System identification (93B30) Discrete-time control/observation systems (93C55) Linear systems in control theory (93C05) Eigenvalue problems (93B60) Asymptotic stability in control theory (93D20)

Related Items (6)

Dissipativity learning control (DLC): theoretical foundations of input-output data-driven model-free control ⋮ Determining optimal input-output properties: a data-driven approach ⋮ Non-parametric methods for \({\mathcal L}_2\)-gain estimation using iterative experiments ⋮ Nonparametric methods for the estimation of \(\ell_2\) gain and gains at sample frequencies using bandpass filters ⋮ Data-driven estimation of the lower bounds of gain and phase margins ⋮ Analyzing iterations in identification with application to nonparametric \(\mathcal H_{\infty}\)-norm estimation

Cites Work

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• Non-parametric methods for \({\mathcal L}_2\)-gain estimation using iterative experiments
• On covariance function tests used in system identification
• A fast algorithm to compute the \(H_{\infty}\)-norm of a transfer function matrix
• Conjugate gradient algorithm for optimization under unitary matrix constraint
• Fast projection methods for minimal design problems in linear system theory
• Comparing different approaches to model error modeling in robust identification
• From experiment design to closed-loop control
• Estimation of the risk for an unstable behaviour of feedback systems in the presence of nonlinear distortions
• Data-driven model reference control with asymptotically guaranteed stability
• Asymptotic properties of the least-squares method for estimating transfer functions and disturbance spectra
• The Geometry of Algorithms with Orthogonality Constraints
• A time-domain approach to model validation
• A family of norms for system identification problems

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