Fuzzy neural network-based adaptive impedance force control design of robot manipulator under unknown environment

Wei Chen Wang, Ching Hung Lee

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

9 Scopus citations

Abstract

In this paper, an adaptive impedance force control scheme for an n-link robot manipulator under unknown environment is proposed. The system dynamics of the robot manipulator is assumed that system model is not exactly known or has system uncertainty. Therefore, the traditional adaptive impedance force controller is not valid. Herein, the fuzzy neural networks are adopted to estimate the system model terms of robot and the force tracking control is developed by the proposed adaptive scheme. The proposed scheme is established by gradient descent approach. Using the Lyapunov stability theory, the update laws of fuzzy neural networks can be derived and the stability of the closed-loop system is guaranteed. Finally, simulation results of a two-link robot manipulator with environment constraint are introduced to illustrate the performance and effectiveness of our approach.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1442-1448
Number of pages7
ISBN (Electronic)9781479920723
DOIs
StatePublished - 4 Sep 2014
Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

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    Wang, W. C., & Lee, C. H. (2014). Fuzzy neural network-based adaptive impedance force control design of robot manipulator under unknown environment. In Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE (pp. 1442-1448). [6891669] (IEEE International Conference on Fuzzy Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2014.6891669