@inproceedings{0a635383594d44cd806ecbaa852d0172,
title = "Learning Unknown Lagrange Dynamical Systems with Guaranteed Persistency of Excitation",
abstract = "In this paper, we present a methodology that ensures a priori that all possible unknown dynamics of the system within a compact set of operation will be excited. A controller is used to make sure that the system with unknown dynamics will follow the reference trajectory and Radial Basis Function (RBF) neural networks are employed to estimate the unknown nonlinearities. The persistency of excitation condition is guaranteed as a prerequisite to achieve accurate estimation of the unknown nonlinear terms and efficient learning. A simulation example clarifies the proposed approach and verifies the aforementioned assertions.",
keywords = "Persistency of Excitation, Prescribed Performance Control, RBF Neural Networks, System Identification",
author = "A. Samanis and Trakas, {P. S.} and X. Papageorgiou and Kyriakopoulos, {K. J.} and Bechlioulis, {C. P.}",
note = "Funding Information: This work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the second call for research projects to support post-doctoral researchers (HFRI-PD19-370). Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Conference on Systems and Control, ICSC 2022 ; Conference date: 23-11-2022 Through 25-11-2022",
year = "2022",
doi = "10.1109/ICSC57768.2022.9993912",
language = "English (US)",
series = "2022 10th International Conference on Systems and Control, ICSC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "372--378",
editor = "Driss Mehdi and Rachid Outbib and Ahmed El-Hajjaji and Eric Busvelle and Hassan Noura",
booktitle = "2022 10th International Conference on Systems and Control, ICSC 2022",
}