Parameter identification of linear time‐invariant systems using dynamic regressor extension and mixing

DOI10.1002/acs.3006zbMath1425.93266OpenAlexW2946643446WikidataQ127828152 ScholiaQ127828152MaRDI QIDQ5240986

No author found.

Publication date: 29 October 2019

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.3006

zbMATH Keywords

system identificationtransient performancepersistent excitationDREM

Mathematics Subject Classification ID

Adaptive control/observation systems (93C40) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05) Identification in stochastic control theory (93E12)

Related Items (7)

Normalization of regressor excitation in the dynamic extension and mixing procedure ⋮ A new on-line exponential parameter estimator without persistent excitation ⋮ Fixed-time estimation of parameters for non-persistent excitation ⋮ I-DREM: relaxing the square integrability condition ⋮ Predefined-time parameter estimation via modified dynamic regressor extension and mixing ⋮ Necessary and sufficient regressor conditions for the global asymptotic stability of recursive least squares ⋮ Accelerated convergence with improved robustness for discrete-time parameter estimation

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A robust globally convergent position observer for the permanent magnet synchronous motor
• On dynamic regressor extension and mixing parameter estimators: two Luenberger observers interpretations
• Relaxing the high-frequency gain sign assumption in direct model reference adaptive control
• On global asymptotic stability of \(\dot{x}=-\phi(t)\phi^\top(t)x\) with \(\phi\) not persistently exciting
• Exponential stability of linear equations arising in adaptive identification
• Adaptive observers with exponential rate of convergence
• Identification of photovoltaic arrays' maximum power extraction point via dynamic regressor extension and mixing
• A robust nonlinear position observer for synchronous motors with relaxed excitation conditions
• Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing

This page was built for publication: Parameter identification of linear time‐invariant systems using dynamic regressor extension and mixing