Learning Unknown Lagrange Dynamical Systems with Guaranteed Persistency of Excitation

A. Samanis, P. S. Trakas, X. Papageorgiou, K. J. Kyriakopoulos, C. P. Bechlioulis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2022 10th International Conference on Systems and Control, ICSC 2022
EditorsDriss Mehdi, Rachid Outbib, Ahmed El-Hajjaji, Eric Busvelle, Hassan Noura
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages372-378
Number of pages7
ISBN (Electronic)9781665465076
DOIs
StatePublished - 2022
Event10th International Conference on Systems and Control, ICSC 2022 - Marseille, France
Duration: Nov 23 2022Nov 25 2022

Publication series

Name2022 10th International Conference on Systems and Control, ICSC 2022

Conference

Conference10th International Conference on Systems and Control, ICSC 2022
Country/TerritoryFrance
CityMarseille
Period11/23/2211/25/22

Keywords

  • Persistency of Excitation
  • Prescribed Performance Control
  • RBF Neural Networks
  • System Identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

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