Optimal input design for direct data-driven tuning of model-reference controllers

DOI10.1016/j.automatica.2013.02.054zbMath1360.93722OpenAlexW2015893195WikidataQ58220189 ScholiaQ58220189MaRDI QIDQ522847

Alireza Karimi, Simone Formentin, Sergio M. Savaresi

Publication date: 19 April 2017

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2013.02.054

zbMATH Keywords

input designidentification for controldata-driven controlcorrelation-based tuning (CbT)virtual reference feedback tuning (VRFT)

Mathematics Subject Classification ID

Feedback control (93B52) Design techniques (robust design, computer-aided design, etc.) (93B51) Identification in stochastic control theory (93E12)

Related Items (9)

Enhancing feedforward controller tuning via instrumental variables: with application to nanopositioning ⋮ Data-driven design of two degree-of-freedom nonlinear controllers: the \(\operatorname{D}^2\)-IBC approach ⋮ An easily understandable grey wolf optimizer and its application to fuzzy controller tuning ⋮ Direct data‐driven design of switching controllers ⋮ Direct learning of LPV controllers from data ⋮ Enhancing statistical performance of data-driven controller tuning via \(\mathcal{L}_2-\)regularization ⋮ Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm ⋮ Optimal direct data-driven control with stability guarantees ⋮ Deterministic continuous-time virtual reference feedback tuning (VRFT) with application to PID design

Cites Work

• Instrumental variable methods for system identification
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• From experiment design to closed-loop control
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• Inequalities in Hilbert Modules of Matrix-Valued Functions
• Iterative feedback tuning—an overview
• Iterative correlation‐based controller tuning
• A survey of spectral factorization methods
• Prefiltering in Iterative Feedback Tuning: Optimization of the Prefilter for Accuracy
• Optimal prefiltering in iterative feedback tuning
• Direct Nonlinear Control Design: The Virtual Reference Feedback Tuning (VRFT) Approach

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