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

doi:10.1109/COASE.2018.8560584

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
Publisher	IEEE Computer Society
Pages	1284-1289
Number of pages	6
ISBN (Electronic)	9781538635933
DOIs	https://doi.org/10.1109/COASE.2018.8560584
State	Published - 4 Dec 2018
Externally published	Yes
Event	14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany Duration: 20 Aug 2018 → 24 Aug 2018

Publication series

Name	IEEE International Conference on Automation Science and Engineering
Volume	2018-August
ISSN (Print)	2161-8070
ISSN (Electronic)	2161-8089

Conference

Conference	14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Country	Germany
City	Munich
Period	20/08/18 → 24/08/18

Access to Document

10.1109/COASE.2018.8560584

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Li, K., Nuchkrua, T., Zhao, H., Yuan, Y., & Boonto, S. (2018). Learning-based Adaptive Robust Control of Manipulated Pneumatic Artificial Muscle Driven by H ₂ -based Metal Hydride In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018 (pp. 1284-1289). [8560584] (IEEE International Conference on Automation Science and Engineering; Vol. 2018-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2018.8560584

Li, Kelin ; Nuchkrua, Thanana ; Zhao, Huan ; Yuan, Ye ; Boonto, Sudchai. / Learning-based Adaptive Robust Control of Manipulated Pneumatic Artificial Muscle Driven by H ₂ -based Metal Hydride 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. IEEE Computer Society, 2018. pp. 1284-1289 (IEEE International Conference on Automation Science and Engineering).

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

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.",

author = "Kelin Li and Thanana Nuchkrua and Huan Zhao and Ye Yuan and Sudchai Boonto",

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doi = "10.1109/COASE.2018.8560584",

language = "英语",

series = "IEEE International Conference on Automation Science and Engineering",

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Li, K, Nuchkrua, T, Zhao, H, Yuan, Y & Boonto, S 2018, Learning-based Adaptive Robust Control of Manipulated Pneumatic Artificial Muscle Driven by H ₂ -based Metal Hydride in 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018., 8560584, IEEE International Conference on Automation Science and Engineering, vol. 2018-August, IEEE Computer Society, pp. 1284-1289, 14th IEEE International Conference on Automation Science and Engineering, CASE 2018, Munich, Germany, 20/08/18. https://doi.org/10.1109/COASE.2018.8560584

Learning-based Adaptive Robust Control of Manipulated Pneumatic Artificial Muscle Driven by H ₂ -based Metal Hydride . / Li, Kelin; Nuchkrua, Thanana; Zhao, Huan; Yuan, Ye; Boonto, Sudchai.

2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. IEEE Computer Society, 2018. p. 1284-1289 8560584 (IEEE International Conference on Automation Science and Engineering; Vol. 2018-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Li K, Nuchkrua T, Zhao H, Yuan Y, Boonto S. Learning-based Adaptive Robust Control of Manipulated Pneumatic Artificial Muscle Driven by H ₂ -based Metal Hydride In 2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018. IEEE Computer Society. 2018. p. 1284-1289. 8560584. (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/COASE.2018.8560584