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Self-Sensing Actuator Using an Ion-Polymer Metal Composite Based on a Neural Network Model

뉴럴네트워크 모델 기반의 IPMC 셀프 센싱 액추에이터

  • Yoon, Jong-Il (Dept. of Mechanical and Automotive Engineering, Univ. of Ulsan.) ;
  • Truong, Dinh Quang (School of Mechanical and Automotive Engineering, Univ. of Ulsan.) ;
  • Ahn, Kyoung-Kwan (School of Mechanical and Automotive Engineering, Univ. of Ulsan.)
  • 윤종일 (울산대학교 기계자동차공학과) ;
  • 딩광졍 (울산대학교 기계자동차공학부) ;
  • 안경관 (울산대학교 기계자동차공학부)
  • Received : 2010.07.05
  • Accepted : 2010.10.20
  • Published : 2010.12.01

Abstract

We develop an IPMC actuator with self-sensing behavior based on an accurate neural network model (NNM). The supplied voltage and voltage signals measured at two determined points on both sides of the IPMC sheet are used as inputs to the NNM. A CCD laser displacement sensor is installed in the rig for accurate measurement of the IPMC tip displacement that is used as the training output of the proposed NNM. Consequently, the NNM model is used to estimate the IPMC tip displacement; the NNM parameters are optimized by the collected input/output training data. The effectiveness of the model for the IPMC actuator is then verified by modeling results.

Keywords

IPMC;Self-Sensing;NNM;RMLPNN;SALM

Acknowledgement

Supported by : 한국연구재단

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