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

Mohd Azuwan Mat Dzahir, Shinichirou Yamamoto

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number538
JournalApplied Sciences (Switzerland)
Volume9
Issue number12
DOIs
Publication statusPublished - 2019 Jun 1

Fingerprint

muscles
Particle swarm optimization (PSO)
Muscle
optimization
Hysteresis
hysteresis
nonlinearity
Patient rehabilitation
Control nonlinearities
Condition monitoring
irregularities
MATLAB
therapy
friction
platforms
Friction
augmentation
excitation
simulation

Keywords

  • Empirical modeling
  • McKibben muscle
  • Particle swarm optimization

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

Dynamic modeling of McKibben muscle using empirical model and particle swarm optimization method. / Dzahir, Mohd Azuwan Mat; Yamamoto, Shinichirou.

In: Applied Sciences (Switzerland), Vol. 9, No. 12, 538, 01.06.2019.

Research output: Contribution to journalArticle

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