Adaptation Learning Speed Control for a High- Performance Induction Motor using Neural Networks
This paper proposes an effective adaptation learning
algorithm based on artificial neural networks for speed control of an
induction motor assumed to operate in a high-performance drives
environment. The structure scheme consists of a neural network
controller and an algorithm for changing the NN weights in order that
the motor speed can accurately track of the reference command. This
paper also makes uses a very realistic and practical scheme to
estimate and adaptively learn the noise content in the speed load
torque characteristic of the motor. The availability of the proposed
controller is verified by through a laboratory implementation and
under computation simulations with Matlab-software. The process is
also tested for the tracking property using different types of reference
signals. The performance and robustness of the proposed control
scheme have evaluated under a variety of operating conditions of the
induction motor drives. The obtained results demonstrate the
effectiveness of the proposed control scheme system performances,
both in steady state error in speed and dynamic conditions, was found
to be excellent and those is not overshoot.
[1] C.M.Liaw, Ys.Kung and C.M Wa, Design and implementation of a hight
performance field-oriented induction motor drive. IEEE Trans. Ind.
Electron. Vol 38,4, pp 275-282, 1991.
[2] Jie Zhang and T.H Burton, New approach to field orientation control of
CSI induction motor drive. IEE Proccedings, Vol 135, No 1, January
1988.
[3] P.Pillay and R.Krishnam, Modelling of permanent magnet motor drive.
IEEE Trans. Ind. Electronics. Vol 35. No 4, pp 537-541, 1988.
[4] Tien Chi Chen and Tsong Terng Shen, Model reference neural network
controller for induction motor speed. IEEE Trans energy conversion. Vol
17 N0 2, pp 3301-3305, 2004.
[5] T.C Chen and T.T Sheu, Robust speed controlled induction motor drive
based on model reference with neural networks. Inter. Journ of
Knowledge based Intelligent Engineering System. Vol 3.3 pp 162- 171,
1992.
[6] K.S Narenda, Neural networks for control: Theory and practice.
Proceedings of the IEEE, 84,10 pp 1385-1406, 1996.
[7] M.Kuchar, P.Brandstetter, M.Kaduch, Sensorless, Induction motor drive
with neural network 35th conference pp 3301-3305, 2004.
[8] Y.A.Kwon and S.Hwankin, A new scheme for speed sensorless control
of induction motor. IEEE Trans Ind Electronics, Vol 51 No3, pp 545-
550, 2004.
[9] W.S.Oh, K.M.Cho, S.Kun and H.J.Kim, Optimized neural network
speed control of induction motor using genetic algorithm. IEEE
International Symposwm on Power Electronic, Electrical drives,
Automation and Motion, pp 12-15, SPEEDAM 2006.
[10] N.Bongkyong, L.Hjoon and P.Sanglong, A multiobjective evolutionary
neuro-controller for non-minimum phases system. IEICE Trans INF &
Syst Vol.E87 D, No11 pp 2517-2520, 2004.
[1] C.M.Liaw, Ys.Kung and C.M Wa, Design and implementation of a hight
performance field-oriented induction motor drive. IEEE Trans. Ind.
Electron. Vol 38,4, pp 275-282, 1991.
[2] Jie Zhang and T.H Burton, New approach to field orientation control of
CSI induction motor drive. IEE Proccedings, Vol 135, No 1, January
1988.
[3] P.Pillay and R.Krishnam, Modelling of permanent magnet motor drive.
IEEE Trans. Ind. Electronics. Vol 35. No 4, pp 537-541, 1988.
[4] Tien Chi Chen and Tsong Terng Shen, Model reference neural network
controller for induction motor speed. IEEE Trans energy conversion. Vol
17 N0 2, pp 3301-3305, 2004.
[5] T.C Chen and T.T Sheu, Robust speed controlled induction motor drive
based on model reference with neural networks. Inter. Journ of
Knowledge based Intelligent Engineering System. Vol 3.3 pp 162- 171,
1992.
[6] K.S Narenda, Neural networks for control: Theory and practice.
Proceedings of the IEEE, 84,10 pp 1385-1406, 1996.
[7] M.Kuchar, P.Brandstetter, M.Kaduch, Sensorless, Induction motor drive
with neural network 35th conference pp 3301-3305, 2004.
[8] Y.A.Kwon and S.Hwankin, A new scheme for speed sensorless control
of induction motor. IEEE Trans Ind Electronics, Vol 51 No3, pp 545-
550, 2004.
[9] W.S.Oh, K.M.Cho, S.Kun and H.J.Kim, Optimized neural network
speed control of induction motor using genetic algorithm. IEEE
International Symposwm on Power Electronic, Electrical drives,
Automation and Motion, pp 12-15, SPEEDAM 2006.
[10] N.Bongkyong, L.Hjoon and P.Sanglong, A multiobjective evolutionary
neuro-controller for non-minimum phases system. IEICE Trans INF &
Syst Vol.E87 D, No11 pp 2517-2520, 2004.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59104", author = "M. Zerikat and S. Chekroun", title = "Adaptation Learning Speed Control for a High- Performance Induction Motor using Neural Networks", abstract = "This paper proposes an effective adaptation learning
algorithm based on artificial neural networks for speed control of an
induction motor assumed to operate in a high-performance drives
environment. The structure scheme consists of a neural network
controller and an algorithm for changing the NN weights in order that
the motor speed can accurately track of the reference command. This
paper also makes uses a very realistic and practical scheme to
estimate and adaptively learn the noise content in the speed load
torque characteristic of the motor. The availability of the proposed
controller is verified by through a laboratory implementation and
under computation simulations with Matlab-software. The process is
also tested for the tracking property using different types of reference
signals. The performance and robustness of the proposed control
scheme have evaluated under a variety of operating conditions of the
induction motor drives. The obtained results demonstrate the
effectiveness of the proposed control scheme system performances,
both in steady state error in speed and dynamic conditions, was found
to be excellent and those is not overshoot.", keywords = "Electric drive, Induction motor, speed control,
Adaptive control, neural network, High Performance.", volume = "2", number = "9", pages = "1992-6", }