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BP neural network tuned PID controller for position tracking of a pneumatic artificial muscle

Issue title: Papers from the 3rd International Conference on Biomedical Engineering and Technology (iCBEB 2014), 25–28 September 2014, Beijing, China

Subtitle:

Guest editors: Edward J. Ciaccio

Article type: Research Article

Authors: Fan, Jizhuang | Zhong, Jun | Zhao, Jie | Zhu, Yanhe*

Affiliations: State Key Laboratory of Robotics Institute, Harbin Institute of Technology University, Harbin, Heilongjiang, China

Correspondence: [*] Corresponding author: Yanhe Zhu, State Key Laboratory of Robotics Institute, Harbin Institute of Technology University, Room 203, Building C1, Science Square, Yikuang Street No. 2, Nangang District, Harbin 150006, Heilongjiang, China. Tel.: +86 13074594165; Fax: +86 0451 86414538; E-mail:yhzhu@hit.edu

Keywords: Mckibben muscles, empirical modeling, BP neural network tuned PID, cascade position controller

DOI: 10.3233/THC-150958

Journal: Technology and Health Care, vol. 23, no. s2, pp. S231-S238, 2015

Published: 2015
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Abstract

BACKGROUND: Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties.

OBJECTIVE: This paper deals with position control of PAM.

METHODS: To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics.

RESULTS: Experimental results show high tracking accuracy as compared with a convention PID controller.

CONCLUSION: The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot.

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