Paper
4 April 2007 Self-sensing McKibben actuators using dielectric elastomer sensors
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Abstract
In this paper, a self-sensing McKibben actuator using dielectric elastomer sensors is presented. Fiber-reinforced cylindrical actuators offer one potential solution to the low-force output problem that plagues many artificial muscle actuators. Placing a cylindrical dielectric elastomer sensor in direct contact with the inner surface of the McKibben actuator facilitates in situ monitoring of actuator strains and loads. The deformation of the McKibben actuator and hence the cylindrical dielectric elastomer sensor results in a change in the electrical signal read from the electroded surfaces of the dielectric elastomer. In this paper, we present a model for predicting the response of fiber reinforced cylindrical constructs (McKibben actuators) that are actuated by an inflation pressure, which is used to support an axial load. The model is based on Adkins and Rivlin's large deformation model for the inflation and contraction of tubes reinforced with inextensible fibers. In this model, the McKibben actuator is considered as a surface of revolution since the initially near cylindrical shape is nearly always compromised during mechanical loading. A series of experiments measuring the force versus contraction behavior of the actuators are used to validate the numerical model. The material constants for an Ogden model were determined by uni-axial extension of cylindrical samples. A comparison of the numerical and experimental results shows that the correlation is good. The model enables a number of key analyses such as the effect of the braid angle and the tension generated in the fibers.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
N. C. Goulbourne, S. Son, and J. W. Fox "Self-sensing McKibben actuators using dielectric elastomer sensors", Proc. SPIE 6524, Electroactive Polymer Actuators and Devices (EAPAD) 2007, 652414 (4 April 2007); https://doi.org/10.1117/12.716274
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Cited by 30 scholarly publications.
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KEYWORDS
Actuators

Sensors

Dielectrics

Capacitance

Data modeling

Electrodes

Statistical modeling

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