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A neural network for parameter estimation of a DC motor for feed-drives

  • Part V: Robotics, Adaptive Autonomous Agents, and Control
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

The monitoring of machine-tools implicated in the metal cutting process is the subject of increasing developments because of demands on control, reliability, availability of machine-tools and on the work-piece quality. The use of computers contributes to a better machine and process monitoring by enabling the implementation of complex algorithms for control, monitoring,...

The nonlinear behavior of the main components of the machine-tools: the feed-drives and the spindles, makes the estimation of their fault sensitive physical parameters, difficult to do accurately. As the Artificial Neural Networks (ANNs) are able to model nonlinear process, they might be able to model a parameter estimator. We hope to estimate the physical parameters of feed-drives or spindles by a neural estimator. But before trying this, we have tested the ability of ANNs to estimate the physical parameters of a simple system: a DC motor. The results of this test are presented here.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Desforges, X., Habbadi, A. (1997). A neural network for parameter estimation of a DC motor for feed-drives. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020263

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  • DOI: https://doi.org/10.1007/BFb0020263

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

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