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Analytical modeling and control of soft fast pneumatic networks actuators

Analytical modeling and control of soft fast pneumatic networks actuators
Analytical modeling and control of soft fast pneumatic networks actuators
The soft fast pneumatic networks actuator (fPNA) featured as large-amplitude motion, and long life span provides a promising solution for varieties of innovative applications, such as the rehabilitation glove, the soft gripper, and the multi-gait robot. However, the infinite freedom in theory impedes its modeling for high-precision control. This paper proposes an analytical model of the fPNA based on the principle of the minimum potential energy. The tight integration of computationally efficiency into the analytical inverse solution of the proposed model enables the model-based control of the fPNA. The validation of the model is experimentally verified by four elaborate fPNAs. Furthermore, an inverse model-based iterative learning controller (ILC) is also constructed for position tracking control of the fPNA
IEEE
Cao, Guizhou
bf9a0ae4-ba0d-47bf-95a8-c8258f0c85fd
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Liu, Yanhong
7bb86db3-675f-4692-bebf-b8fd087e2f40
Cao, Guizhou
bf9a0ae4-ba0d-47bf-95a8-c8258f0c85fd
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Liu, Yanhong
7bb86db3-675f-4692-bebf-b8fd087e2f40

Cao, Guizhou, Chu, B. and Liu, Yanhong (2020) Analytical modeling and control of soft fast pneumatic networks actuators. In IECON Proceedings (Industrial Electronics Conference). IEEE.. (doi:10.1109/IECON43393.2020.9254517).

Record type: Conference or Workshop Item (Paper)

Abstract

The soft fast pneumatic networks actuator (fPNA) featured as large-amplitude motion, and long life span provides a promising solution for varieties of innovative applications, such as the rehabilitation glove, the soft gripper, and the multi-gait robot. However, the infinite freedom in theory impedes its modeling for high-precision control. This paper proposes an analytical model of the fPNA based on the principle of the minimum potential energy. The tight integration of computationally efficiency into the analytical inverse solution of the proposed model enables the model-based control of the fPNA. The validation of the model is experimentally verified by four elaborate fPNAs. Furthermore, an inverse model-based iterative learning controller (ILC) is also constructed for position tracking control of the fPNA

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More information

Published date: 21 October 2020
Venue - Dates: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Online, Singapore, 2020-10-18 - 2020-10-21

Identifiers

Local EPrints ID: 472448
URI: http://eprints.soton.ac.uk/id/eprint/472448
PURE UUID: db87fd57-f493-4265-ae11-a2ce14211fd1
ORCID for B. Chu: ORCID iD orcid.org/0000-0002-2711-8717

Catalogue record

Date deposited: 05 Dec 2022 18:09
Last modified: 17 Mar 2024 03:28

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Contributors

Author: Guizhou Cao
Author: B. Chu ORCID iD
Author: Yanhong Liu

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