L2-Gain Tuning for the Gradient Descent Algorithm in the Presence of Disturbances
- Due to its simplicity and inexpensive computation, the gradient descent algorithm is one of the most used tools in adaptive control and system identification. Although it has been studied for decades, little has been achieved in terms of tuning methods in the presence of disturbances. One of the main difficulties in its analysis is the time-varying nature of the algorithm. In this work, we contribute in such direction by providing LMI tools for tuning the gradient descent algorithm gain such that a guaranteed upper bound on the L2 -gain with respect to parameter variations and measurement noise is achieved. Two academic examples are provided to illustrate the efficient application of the method.
Author: | Juan G. Rueda-EscobedoORCiD, Jaime A. Moreno, Johannes SchifferORCiD |
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URL: | https://ieeexplore.ieee.org/document/9838051 |
DOI: | https://doi.org/10.23919/ECC55457.2022.9838051 |
ISBN: | 978-3-9071-4407-7 |
Title of the source (English): | 2022 European Control Conference (ECC) |
Publisher: | IEEE |
Place of publication: | Piscataway |
Document Type: | Conference publication peer-reviewed |
Language: | English |
Year of publication: | 2022 |
Tag: | Gain measurement; Minimization; Upper bound; noise measurement; robustness; system identification |
First Page: | 1610 |
Last Page: | 1616 |
Faculty/Chair: | Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Regelungssysteme und Netzleittechnik |