Higher Derivatives Newton-Based Extremum Seeking for Constrained Inputs

Farzaneh Karimi*, Hossein Ramezani, Roozbeh Izadi-Zamanabadi, Mohsen Mojiri

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

7 Downloads (Pure)

Abstract

This paper introduces a fast learning mechanism to address constrained input in the higher derivatives Newton-based extremum seeking. The proposed algorithm has a two-time-scale structure consisting of: a compensation mechanism (i.e. an anti-windup compensator) with fast dynamics that compensates for the effect of the constrained input, and a slow subsystem to maximize the map’s higher derivatives by regulating the output. The practical asymptotic stability of the new ES algorithm is proved using a modified version of the singular perturbation method. The effectiveness of the proposed algorithm is demonstrated using simulations.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Number of pages6
PublisherIEEE
Publication date9 Oct 2022
Pages1566-1571
Article number9945301
ISBN (Print)978-1-6654-5259-5
ISBN (Electronic)9781665452588
DOIs
Publication statusPublished - 9 Oct 2022
Event2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) - Prague, Czech Republic
Duration: 9 Oct 202212 Oct 2022

Conference

Conference2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Country/TerritoryCzech Republic
CityPrague
Period09/10/202212/10/2022
SeriesI E E E International Conference on Systems, Man, and Cybernetics. Conference Proceedings
ISSN1062-922X

Fingerprint

Dive into the research topics of 'Higher Derivatives Newton-Based Extremum Seeking for Constrained Inputs'. Together they form a unique fingerprint.

Cite this