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Genetic Programming of an Artificial Neural Network for Robust Control of a 2-D Path Following Robot

[+] Author Affiliations
Anthony M. Roy, Erik K. Antonsson, Andrew A. Shapiro

California Institute of Technology, Pasadena, CA

Paper No. DETC2008-50137, pp. 799-805; 7 pages
doi:10.1115/DETC2008-50137
From:
  • ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 34th Design Automation Conference, Parts A and B
  • Brooklyn, New York, USA, August 3–6, 2008
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4325-3 | eISBN: 0-7918-3831-5
  • Copyright © 2008 by ASME

abstract

Genetic Programs that have phenotypes created by the application of genotypes comprising rules are robust and highly scalable. Such encodings are useful for complex applications such as controller design. This paper outlines an evolutionary algorithm capable of creating a controller for 2 DOF, path following robot. The controllers are embodied by Artificial Neural Networks capable of full functionality despite multiple failures.

Copyright © 2008 by ASME

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