Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
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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
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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.

이 논문에서는 뉴럴네트워크 모델에 기초하여 셀프 센싱이 가능한 IPMC 액추에이터를 개발하고자 한다. IPMC의 양면에 있는 두 개의 지정된 점에서 측정된 입력 전압과 입력 신호들을 뉴럴네트워크 모델의 입력 신호로 사용한다. CCD 레이저 변위 센서는 제시된 뉴럴네트워크 모델의 학습된 출력값으로 사용되는 IPMC 끝의 변위를 정확히 측정하기 위해 설치된다. 결과적으로 뉴럴네트워크 모델은 수집된 입력/출력 학습데이터에 의해 최대한으로 활용된 IPMC의 끝의 변위를 평가하기 위해 만들어진다. IPMC 액추에이터를 위해 설계된 모델의 효율성은 결과들을 모델링함으로서 증명되어진다.

Keywords

References

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