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Dynamic modeling of McKibben muscle using empirical model and particle swarm optimization method

Mat Dzahir, Mohd. Azuwan and Yamamoto, Shinichiroh (2019) Dynamic modeling of McKibben muscle using empirical model and particle swarm optimization method. Applied Sciences (Switzerland), 9 (12). ISSN 2076-3417


Official URL: http://dx.doi.org/10.3390/app9122538


This paper explores empirical modeling of McKibben muscle in characterizing its hysteresis behavior and nonlinearities during quasi-static, quasi-rate, and historic dependencies. The unconventional materials-based actuating system called McKibben muscle has excellent properties of power-to-weight ratio, which could be used in rehabilitation orthosis application for condition monitoring, physical enhancement, and rehabilitation therapy. McKibben muscle is known to exhibit hysteresis behavior and it is rate-dependent (the level of hysteresis depends closely on rate of input excitation frequency). This behavior is undesirable and it must be considered in realizing high precision control application. In this paper, the nonlinearities of McKibben muscle is characterized using empirical modeling with multiple correction functions such as shape irregularity and slenderness. A particle swarm optimization (PSO) method is used to determine the best parametric values of the proposed empirical with modified dynamic friction model. The LabVIEW and MATLAB platforms are used for data analysis, modeling and simulation. The results confirm that this model able to significantly characterize the nonlinearities of McKibben muscle while considering all dependencies.

Item Type:Article
Uncontrolled Keywords:empirical modeling, McKibben muscle, particle swarm optimization
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:87909
Deposited By: Yanti Mohd Shah
Deposited On:30 Nov 2020 21:36
Last Modified:30 Nov 2020 21:36

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