@inproceedings{8d21199245f34785879ce5fe939a0438,
title = "Higher Derivatives Newton-Based Extremum Seeking for Constrained Inputs",
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{\textquoteright}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.",
keywords = "Perturbation methods, Stability analysis, Learning systems",
author = "Farzaneh Karimi and Hossein Ramezani and Roozbeh Izadi-Zamanabadi and Mohsen Mojiri",
year = "2022",
month = oct,
day = "9",
doi = "10.1109/SMC53654.2022.9945301",
language = "English",
isbn = "978-1-6654-5259-5",
series = "I E E E International Conference on Systems, Man, and Cybernetics. Conference Proceedings",
publisher = "IEEE",
pages = "1566--1571",
booktitle = "2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
address = "United States",
note = "2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ; Conference date: 09-10-2022 Through 12-10-2022",
}