A comparison of model-based and data-driven controller tuning

DOI10.1002/acs.2415zbMath1337.93016OpenAlexW1841556254WikidataQ58220149 ScholiaQ58220149MaRDI QIDQ2802036

Klaske van Heusden, Simone Formentin, Alireza Karimi

Publication date: 22 April 2016

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.2415

zbMATH Keywords

accuracy analysismodel reference controldata-driven controller tuning

Mathematics Subject Classification ID

Design techniques (robust design, computer-aided design, etc.) (93B51) Linear systems in control theory (93C05) Stochastic systems in control theory (general) (93E03)

Related Items (7)

Enhancing feedforward controller tuning via instrumental variables: with application to nanopositioning ⋮ Toward a model-free feedback control synthesis for treating acute inflammation ⋮ Direct data‐driven design of switching controllers ⋮ Data-driven fixed-structure frequency-based \(\mathscr{H}_2\) and \(\mathscr{H}_\infty\) controller design ⋮ Data-driven predictive control in a stochastic setting: a unified framework ⋮ Limited-complexity controller tuning: a set membership data-driven approach ⋮ Deterministic continuous-time virtual reference feedback tuning (VRFT) with application to PID design

Uses Software

• Matlab
• System Identification Toolbox

Cites Work

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• The filter design from data (FD2) problem: parametric-statistical approach
• On identification methods for direct data-driven controller tuning
• Data-driven model reference control with asymptotically guaranteed stability
• The unfalsified control concept and learning
• Virtual reference direct design method: an off-line approach to data-based control system design
• Iterative correlation‐based controller tuning

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