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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural NetworksAvailable to Purchase
ISBN:
9780791802953
No. of Pages:
636
Publisher:
ASME Press
Publication date:
2009
eBook Chapter
63 Adaptive Neural Controller for a Permanent Magnet DC Motor Available to Purchase
By
Rajab Challoo
,
Rajab Challoo
Department of Mechanical Engineering
Texas A&M University-Kingsville
Kingsville, TX
, USA
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R. Palaniswamy
,
R. Palaniswamy
Department of Mechanical Engineering
Texas A&M University-Kingsville
Kingsville, TX
, USA
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S. Li
,
S. Li
Department of Electrical & Computer Engineering
The University of Alabama
Tuscaloosa, AL
, USA
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S. Ozcelik
S. Ozcelik
Department of Mechanical Engineering
Texas A&M University-Kingsville
Kingsville, TX
, USA
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Page Count:
8
-
Published:2009
Citation
Challoo, R, Palaniswamy, R, Li, S, & Ozcelik, S. "Adaptive Neural Controller for a Permanent Magnet DC Motor." Intelligent Engineering Systems through Artificial Neural Networks. Ed. Dagli, CH, Bryden, KM, Corns, SM, Gen, M, Tumer, K, & Süer, G. ASME Press, 2009.
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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.
Abstract
Introduction
Simulink Model of PMDC Motor
Adaptive Neural Controller
Graphical Representation of a Simple Neural Network
Adaptation Algorithm
Neural Network Controller System Block Diagram
Simulink Model of Neural Controller
Simulation of Cascade Control
Simulation Results
Real-Time Implementation
Results
Conclusion
References
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