Ai, Q, Peng, Y, Zuo, J et al. (2 more authors) (2019) Hammerstein model for hysteresis characteristics of pneumatic muscle actuators. International Journal of Intelligent Robotics and Applications, 3 (1). pp. 33-44. ISSN 2366-5971
Abstract
As a kind of novel compliant actuators, pneumatic muscle actuators (PMAs) have been recently used in wearable devices for rehabilitation, industrial manufacturing and other fields due to their excellent actuation characteristics such as high power/weight ratio, safety and inherent compliance. However, the strong nonlinearity and asymmetrical hysteresis cause difficulties in the accurate control of robots actuated by PMAs. In this paper, a method for hysteresis modeling of PMA based on Hammerstein model is proposed, which introduces the BP neural network into the hysteretic system. In order to overcome the limitation of BP neural network’s single-valued mapping, an extended space input method is adapted while the Modified Prandtl–Ishlinskii model is applied to characterize the hysteretic phenomenon. A conventional PID control is implemented to track the trajectory of PMA with and without the feed-forward hysteresis compensation based on Hammerstein model, and experimental results validate the effectiveness of the designed model which has the advantages of high precision and easy identification.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2019, Springer Nature Singapore Pte Ltd. This is an author produced version of a paper published in the International Journal of Intelligent Robotics and Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Pneumatic muscle actuator; Hammerstein model; Prandtl-Ishlinskii model; Asymmetric hysteresis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society ICA\R1\180203 |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 May 2019 13:45 |
Last Modified: | 22 Feb 2020 01:38 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s41315-019-00084-5 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145486 |