Control-Enabling Adaptive Nonlinear System Identification

In this article, we rely on the theoretical foundations of radial basis function neural networks to form an adaptive parameter estimation problem, which we solve using the recently introduced prescribed performance control methodology. Such combination results in a compact user-configurable adaptive...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2022-07, Vol.67 (7), p.3715-3721
Main Authors: Zisis, Konstantinos, Bechlioulis, Charalampos P., Rovithakis, George A.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive identification
Adaptive systems
Control methods
Linear systems
Mathematical model
Neural networks
Nonlinear control
Nonlinear dynamical systems
Nonlinear dynamics
Nonlinear systems
Parameter estimation
persistency of excitation (PE)
Radial basis function
System identification
Trajectory
Transient analysis
Uncertainty
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Summary:In this article, we rely on the theoretical foundations of radial basis function neural networks to form an adaptive parameter estimation problem, which we solve using the recently introduced prescribed performance control methodology. Such combination results in a compact user-configurable adaptive nonlinear system identification methodology that can be used to retrieve the open-loop nonlinear plant dynamics in any compact region of interest.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2021.3106870