Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equati...

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Veröffentlicht in:Applied sciences Jg. 9; H. 2; S. 324
Hauptverfasser: Zha, Fusheng, Sheng, Wentao, Guo, Wei, Qiu, Shiyin, Deng, Jing, Wang, Xin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 17.01.2019
Schlagworte:
Accuracy
Algorithms
Design
dynamic parameter identification
exoskeleton
Experiments
Friction
Hip joint
Hydraulics
Methods
Optimization
Parameter identification
particle swarm optimization
recursive least square
Researchers
Software
ISSN:2076-3417, 2076-3417
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Zusammenfassung:The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton’s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.
Bibliographie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app9020324