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dc.contributor.authorYe, Zhihang
dc.contributor.authorChen, Zheng
dc.identifier.citationYe, Zhihang; Chen, Zheng. 2017. Self-sensing of dielectric elastomer actuator enhanced by artificial neural network. Smart Materials and Structures, vol. 26:no. 9en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractDielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have promising usage in future's soft actuators and sensors, such as soft robotics, energy harvesters, and wearable sensors. In this paper, a stripe DE actuator with integrated sensing capability is designed, fabricated, and characterized. Since the strip actuator can be approximated as a compliant capacitor, it is possible to detect the actuator's displacement by analyzing the actuator's impedance change. An integrated sensing scheme that adds a high frequency probing signal into actuation signal is developed. Electrical impedance changes in the probing signal are extracted by fast Fourier transform algorithm, and nonlinear data fitting methods involving artificial neural network are implemented to detect the actuator's displacement. A series of experiments show that by improving data processing and analyzing methods, the integrated sensing method can achieve error level of lower than 1%.en_US
dc.description.sponsorshipNational Science Foundation under CAREER Grant DCSD #1653301 and Multidisciplinary Research Project Award (MURPA) Wichita State University.en_US
dc.publisherIOP Publishingen_US
dc.relation.ispartofseriesSmart Materials and Structures;v.26:no.9
dc.subjectDielectric elastomeren_US
dc.subjectArtificial neural networken_US
dc.titleSelf-sensing of dielectric elastomer actuator enhanced by artificial neural networken_US
dc.rights.holder© Copyright 2017 IOP Publishingen_US

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