Adaptive RBFNN control of robot manipulators with finite-time convergence

Jing Na, Chenguang Yang, Runxian Yang

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
220 Downloads (Pure)

Abstract

In this paper, the position tracking control with finite-time convergence has been studied for a class of nonlinear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.
Original languageEnglish
Title of host publicationIECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society
Subtitle of host publicationProceedings of a meeting held 24-27 October 2016, Florence, Italy
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages42-47
Number of pages6
ISBN (Electronic)9781509034741
ISBN (Print)9781509034758
DOIs
Publication statusPublished - Mar 2017
EventIECON 2016 : 42nd Annual Conference of the IEEE Industrial Electronics Society - Palazzo dei Congressi, Piazza Adua, 1, Florence, Italy
Duration: 24 Oct 201627 Oct 2016
http://www.iecon2016.org/

Conference

ConferenceIECON 2016
Country/TerritoryItaly
CityFlorence
Period24/10/1627/10/16
Internet address

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