A genetic algorithm approach for model reference adaptive control of ionic polymer metal composites

@article{Lavu2004AGA,
  title={A genetic algorithm approach for model reference adaptive control of ionic polymer metal composites},
  author={B. C. Lavu and M. P. Schoen and Abhijit Mahajan},
  journal={Proceedings of the 2004 American Control Conference},
  year={2004},
  volume={6},
  pages={5514-5519 vol.6}
}
Electroactive polymers undergo physical deformation to external voltage stimuli. These electrically activated polymers possess extraordinary features making them capable as lightweight sensors and actuators in manifold applications. The characteristics of applied voltage and environmental conditions, especially the moisture content surrounding the polymer, have a combined influence on the dynamical behavior of these polymers. In order to characterize these polymers under varying environmental… CONTINUE READING

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Key Quantitative Results

  • 6 and 7 the correlation factor between the desired and simulated tracking was found to be 99.15% and the root mean square error was estimated as 0.2866, respectively.

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