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Intelligent Engineering Systems through Artificial Neural Networks

Editor
Cihan H. Dagli
Cihan H. Dagli
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K. Mark Bryden
K. Mark Bryden
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Steven M. Corns
Steven M. Corns
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Mitsuo Gen
Mitsuo Gen
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Kagan Tumer
Kagan Tumer
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Gürsel Süer
Gürsel Süer
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ISBN:
9780791802953
No. of Pages:
636
Publisher:
ASME Press
Publication date:
2009

The aim of this paper is to show and illustrate, in a simple language, how neural controllers can easily be designed and implemented in real-time to control a system. We first design and simulate an adaptive neural controller to control a Permanent Magnet DC (PMDC) Motor. The controller is divided into two sections — speed error minimizing and current error minimizing and the two neural controllers (speed and current) are modeled in Simulink. Brandt-Lin adaptation algorithm is used to minimize the error signal. The system is then implemented in real-time with a real motor using dSPACE and the control desk software. Finally, the simulation results and the real-time results are compared and discussed.

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