The week's pick
Reseach Article
Induction Motor Speed Control with Fast Response using the Levenberg Marquardt method for Electric Cars
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 42 - Number 13 |
| Year of Publication: 2012 |
| Authors: Dedid Ch, Soebagio, Mauridhi Hery Purnomo |
10.5120/5752-7963
|
Dedid Ch, Soebagio, Mauridhi Hery Purnomo . Induction Motor Speed Control with Fast Response using the Levenberg Marquardt method for Electric Cars. International Journal of Computer Applications. 42, 13 ( March 2012), 14-19. DOI=10.5120/5752-7963
Abstract
Drive system at a low speed electric cars have a hard speed setting is set on a wide range, causing an inconvenience for motorists and a fast response is required any change of speed. It is necessary for good system performance in control motor speed and torque at low speeds which is operated by Field Oriented Control (FOC). In this method of Proportional Integral Derivative used settings Fuzzy Logic Controller (PID-FLC) to dynamically respond to changes in speed and torque in an electric car, so we get smoothness at any speed change and braking as well as maximum torque motor. Neural Network (NN) with Levenberg Marquardt training (LevMar), to set the parameters and the optimal PID gain to drive a three phase induction motor. Test results showed that a fast response to changes in speed electric car.
References
- C. C. Channand K. T. Chau, Member, IEEE, An Overview of Power Electronics in Electric Vehicles, IEEE Transactions on Industrial Electronics, vol. 44, no. 1, February 1997.
- Nobuyoshi Mutoh, Satoru Kaneko, Taizou Miyazaki, Ryosou Masaki, and Sanshiro Obara, A Torque Controller Suitable for Electric Vehicles, IEEE Transactions On Industrial Electronics, vol. 44, no. 1, February 1997
- Mariam Khan, and Narayan C. Kar, Performance Analysis of Fuzzy Based Indirect Field Oriented Control of Induction Motor Drives for Hybrid Electric Vehicles, IEEE,
- Martin T. Hagan and Mohamad B. Menhaj, Training Feedforward with the Marquardt Algorithm, IEEE Transaction on Neural Network, Vol. 5 No. 6 November 1994.
- Manolis I. A. Lourakis, A Brief Description of the Levenberg-Marquardt Algorithm Implemened by levmar, Institute of Computer Science Foundation for Research and Technology - Hellas (FORTH) Vassilika Vouton, P. O. Box 1385, GR 711 10 Heraklion, Crete, GREECE, February 11, 2005
- Sam Roweis. Levenberg-Marquardt Optimi-zation[J].
- Jae-Sung Yu, Sang-Hoon Kim, Fuzzy logic based Vector Control scheme for permanent magnet Synchronous motors in elevator drive applicantion, IEEE Trans on Industrial Electronics, Vol. 54 No. 4, Aug 2007.
- Dynamic Simulation of electric Machinery, Using Matlab/Simulink, Chee-Mung On, Prentice Hall PTR, Upper 5addce River, New Jersey 07458, 1998.
- Mohamed Rachid Chekkouri, Jordi Catal, Fuzzy Adaptive Control of an Induction Motor Drive, ISSN 0005–1144 ATKAAF 44(3–4), 113–122 (2003)
- M. Vasudevan, R. Arumugam, S. Paramasivam, High Performance Adaptive Intelligent Direct Torque Control Schemes for Induction Motor Drives, Serbian Journal Of Electrical Engineering, Vol. 2, No. 1, May 2005, 93 – 116.
- Mamdani,E. H, Application Of Fuzzy Algoritms For Control Of Simple Dynamic Plant, Proc. Of IEEE, 12,1974, h. 1585 – 1588.
- CC Lee, Fuzzy Logic In Control System : Fuzzy Logic Controller Part I, IEEE Trans. On Systems, Man, And Cybernetics,Vol 20 No 2, March – April 1990.
- JF Hurdle, The Synthesis Of Compact Fuzzy Neural Circuits, IEEE Trans. Fuzzy Systems, Vol. 5, No. 1,February 1997, p. 44
- W,Li, A Method for Design of a Hybrid Neuro-Fuzzy Control Systems Based On Behavior Modeling, IEEE Trans. Fuzzy Systems, Vol 5,No1, February 1997, p. 128.
- Z. Q Liu and F Yan, Fuzzy Neural Network in Case Based Diagnostic Systems, IEEE Trans. Fuzzy Systems, Vol 5,No2, may 1997, p. 209
Index Terms
Keywords