Robust feedback error learning method for controller design of nonlinear systems

Hongping Chen, Kotaro Hirasawa, Jinglu Hu

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

Abstract

This paper presents a new robust controller design method for nonlinear system based on feedback error learning (FEL) method and higher order derivatives of Universal Learning Networks (ULNs). Our idea is to make an inverse model robust to signal noise by adding the sensitivity terms to the standard criterion function. Through feedback error learning, the sensitivity term can be minimized as well as usual criterion functions using the higher order derivatives of ULNs. As a result, it is confirmed by using simulation results that NNC robust against signal noise can be obtained.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1835-1840
Number of pages6
DOIs
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume3
ISSN (Print)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period04/7/2504/7/29

ASJC Scopus subject areas

  • Software

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  • Cite this

    Chen, H., Hirasawa, K., & Hu, J. (2004). Robust feedback error learning method for controller design of nonlinear systems. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (pp. 1835-1840). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 3). https://doi.org/10.1109/IJCNN.2004.1380888