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Optimizing Control of Passive Gait Training Exoskeleton Driven by Pneumatic Muscles Using Switch-Mode Firefly Algorithm

Published online by Cambridge University Press:  22 April 2019

Yu Cao
Affiliation:
The Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. E-mails: [email protected], [email protected]
Jian Huang*
Affiliation:
The Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. E-mails: [email protected], [email protected]
Zhangbo Huang
Affiliation:
The Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. E-mails: [email protected], [email protected]
Xikai Tu
Affiliation:
School of Mechanical Engineering, Hubei University of Technology, Wuhan, China. E-mail: [email protected]
Samer Mohammed
Affiliation:
The Laboratory of Images, Signals and Intelligent Systems (LISSI), University Paris-Est Créteil (UPEC), 94400 Vitry Sur Seine, France. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

This paper presents a lower-limb exoskeleton that is actuated by pneumatic muscle actuators (PMAs). This exoskeleton system is composed of the mechanical structures, a treadmill, and a weight support system. With the cooperative work of the three parts, the system aims to assist either the elderly for muscle strengthening by conducting walking activities or the stroke patients during a rehabilitation training program. A mechanism is developed to separate the PMAs from the wearer’s legs to reduce the subject’s physical exertion. Furthermore, considering the difficulty in the modeling of proposed PMAs-driven exoskeleton, a safe and model-free control strategy called proxy-based sliding mode control (PSMC) is used to ensure proper control of the exoskeleton. However, the favorable performances are strongly dependent on the appropriate control parameters, which may be difficult to obtain with blind tuning. Therefore, we propose a global parameters optimization algorithm called switch-mode firefly algorithm (SMFA) to automatically calculate the pre-defined object function and attain the most applicable parameters. Experimental studies are conducted, and the results show the effectiveness of the proposed method.

Type
Articles
Copyright
© Cambridge University Press 2019 

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