Learning-based Adaptive Robust Control of Manipulated Pneumatic Artificial Muscle Driven by H 2 -based Metal Hydride

Kelin Li, Thanana Nuchkrua*, Huan Zhao, Ye Yuan, Sudchai Boonto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Pneumatic artificial muscle(PAM) Hx-based metal hydride (MU) is considered a compact inherent soft actuator. To aim at a high-performance manipulated-PAM based MU actuator, the bottleneck of improving the performance lies in the parametric and nonlinear uncertainties occurred by an unpredictable environment in addition to an inherent nonlinear dynamics of PAM and a large scale dimension of MU. We develop the parameter-based adaptive robust control framework to cope the various operations, where a data-driven learning-based approach is dealt with the parameter adaptation. The effectiveness of our proposed approach is demonstrated through extensive experiments in terms of position and tracking control.

Original languageEnglish
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PublisherIEEE Computer Society
Pages1284-1289
Number of pages6
ISBN (Electronic)9781538635933
DOIs
StatePublished - 4 Dec 2018
Externally publishedYes
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany
Duration: 20 Aug 201824 Aug 2018

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2018-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference14th IEEE International Conference on Automation Science and Engineering, CASE 2018
CountryGermany
CityMunich
Period20/08/1824/08/18

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