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http://dx.doi.org/10.3795/KSME-A.2010.34.12.1865

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

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.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.34, no.12, 2010 , pp. 1865-1870 More about this Journal
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;
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