Research Article
Comparison of AI Based Position Estimation Techniques for Switched Reluctance Motor
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 1 - Issue 26 |
| Published: February 2010 |
| Authors: L.Jessi Sahaya Shanthi, R.Arumugam, M.Nilesh Tawri, S.Prem Kumar |
10.5120/481-791
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| PDF not available |
L.Jessi Sahaya Shanthi, R.Arumugam, M.Nilesh Tawri, S.Prem Kumar . Comparison of AI Based Position Estimation Techniques for Switched Reluctance Motor. International Journal of Computer Applications. 1, 26 (February 2010), 42-48. DOI=10.5120/481-791
@article{ 10.5120/481-791,
author = { L.Jessi Sahaya Shanthi,R.Arumugam,M.Nilesh Tawri,S.Prem Kumar },
title = { Comparison of AI Based Position Estimation Techniques for Switched Reluctance Motor },
journal = { International Journal of Computer Applications },
year = { 2010 },
volume = { 1 },
number = { 26 },
pages = { 42-48 },
doi = { 10.5120/481-791 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2010
%A L.Jessi Sahaya Shanthi
%A R.Arumugam
%A M.Nilesh Tawri
%A S.Prem Kumar
%T Comparison of AI Based Position Estimation Techniques for Switched Reluctance Motor%T
%J International Journal of Computer Applications
%V 1
%N 26
%P 42-48
%R 10.5120/481-791
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper describes the comparison of Artificial Intelligence (AI) based rotor position estimation techniques for Switched Reluctance Motor (SRM) with respect to its execution time in Digital Signal Processor (DSP) TMS320F2812. The various networks of Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System ( ANFIS) structures are trained for mapping the nonlinear current-flux linkage-rotor position characteristics of an 8/6 Switched Reluctance Motor. The trained Artificial Neural Network (ANN) models and the Adaptive Neuro Fuzzy Inference System (ANFIS) structures are implemented on DSP TMS320F2812 to estimate the rotor position from the input current and flux linkage. The execution time of the rotor position estimation algorithms based on the Artificial Neural Network (ANN) models and the Adaptive Neuro Fuzzy Inference System (ANFIS) structures are compared. The execution time limits the use of Artificial Intelligence (AI) based rotor position estimation techniques at high speeds. The nonlinear current-flux linkage-rotor position characteristics are obtained from a test motor .It is a special type of Switched Reluctance Motor (SRM) which has a can arrangement in between stator and rotor to enable liquid cooling and also the rotor is not laminated.
References
- Roberto Cárdenas, Rubén Peña, Marcelo Pérez, Jon Clare,Greg Asher, and Patrick Wheeler, “Power Smoothing Using a Flywheel Driven by a Switched Reluctance Machine” IEEE Transactions on Industrial Electronics, vol. 53, no. 4, pp.1086-1093,August 2006.
- Debiprasad Panda and V. Ramanarayanan, “Reduced acoustic noise variable DC-bus-voltage based sensor less switched reluctance motor drive for HVAC application ”, IEEE Transactions on Industrial Electronics vol. 54, no.4,pp.2065-2078, August 2007.
- Mehrdad Ehsani and Babak Fahimi, “Elimination of position sensors in switched reluctance motor drives: state of the art and Future Trends”, IEEE Transactions on Industrial Electronics, vol. 49, no.1, pp.40-47, February 2002.
- Paul P. Acarnley and John F. Watson, “Review of position-sensorless operation of brushless permanent-magnet machines”, IEEE Transactions on Industrial Electronics, vol. 53, no.2, pp.352-362, April 2006.
- Christopher A. Hudson, N.S.Lobo and R.Krishnan, “Sensorless control of single switch-based switched reluctance motor drive using neural network” IEEE Transactions on Industrial Electronics, vol.55, no.1, pp. 321-329,January 2008.
- . PeterVas,”Artificial-Intelligence-based electrical machines and drives”,Oxford Science publications.
- B.Widrow and M.A.Lehr,”30 years of adaptive neural networks: Perceptron,madaline and backpropagation”,Proc. IEEE,vol.78,no.9,pp 1415-1442,Sep. 1990.
- Won-Sik Baik, Min-Huei Kim, Nam-Hun Kim and Dong-Hee Kim, “Position sensorless control system of SRM using neural network”, 35th Annual IEEE Power Electronics Specialists Conference Aachen, Germany, 2004
- Adrian David Cheok and Nesimi Ertugrul, “High robustness and reliability of fuzzy logic based position estimation for sensorless switched reluctance motor drives”, IEEE Transactionson Power Electronics, vol.15, no.2,pp. 319-334, Mar. 2000.
- T.Lachman,T.R.Mohamad and S.P.Teo, “Sensorless position estimation of switched reluctance motors using artificial neural networks”,inproc.IEEEInt.conf.Robot.,Intell.Syst.Signal.Process.,vol.1,pp.220-225, Oct.2003.
- Wen Ding and Deliang Liang ,” Modeling of a 6/4 switched reluctance motor using adaptive feural fuzzy Inference system”, IEEE Trans. on Magnetics, vol. 44, no. 7, pp. 1796-1804, July 2008.
- S. Paramasivam, S. Vijayan, M. Vasudevan, R. Arumugam and Ramu Krishnan,” Real-time verification of AI based Rotor position estimation techniques for a 6/4 pole switched reluctance motor drive”, IEEE Trans. on Magnetics, vol. 43, no.7, pp. 3209-3222, July 2007.
- Erkan Mese and David A.Torrey, “An approach for sensorless position estimation for switched reluctance motors using artificial neural networks”, IEEE Transactionson Power Electronics, vol.17, no.1,pp. 66-75, January 2002.
- H. Hu and P. Y. Woo, “Fuzzy supervisory sliding-mode and neural network control for robotic manipulators,” IEEE Trans.Ind. Electron., vol. 53, no. 3, pp. 929–940, Jun. 2006.
- C.Elmas,S.Sagiroglu,I.Colak and G.Bal, “Nonlinear modeling of a switched reluctance drive based on neural networks”, in Proc. IEEE Electrotech. Conf., vol.2,pp.809-812, Apr.1994.
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