Open Access Open Access  Restricted Access Subscription or Fee Access

RTDS Implementation and Induction Motor Drive Performance Comparison with P-I, Sliding Mode and Iterative Learning Controller

(*) Corresponding author

Authors' affiliations



This paper implements an Iterative Learning Controller (ILC) for speed control of induction motor drive. First, state feedback linearization technique is applied to the induction motor drive for decoupling speed and flux control loop. It uses induction motor model in a stationary (α-β) reference frame with rotor flux and stator current components as state variables, and P-I control of rotor flux and speed. Since the induction motor drive system has model uncertainties, and it is also sensitive to parameter variation, and load disturbances, a robust control strategy based on sliding mode is designed. But, in sliding mode control, torque ripple is significant. To reduce the torque ripple and improve the transient response further, Iterative Learning Controller is implemented. ILC law consists of two parts. First part is a feedback P-I learning law with initial correction term and a forgetting factor. Second part is a Takagi Sugeno (TS) fuzzy logic based P-I learning controller connected in the feedforward path, to improve the dynamic performance. Three control schemes: P-I, sliding mode and ILC are simulated in SIMULINK environment and implemented with real time digital simulator, RT Lab. Results demonstrate that the performance of ILC is better than the other schemes.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Decoupling Control; Feedback Linearization; Iterative Learning Control; Real Time Digital Simulator; Sliding Mode Controller; Takagi Sugeno Fuzzy Logic

Full Text:



J. J. E. Slotine and W. Li, Applied Nonlinear Control (Prentice Hall, Englewood Cliffs, 1991).

A. Isidori, Nonlinear control Systems (Springer Verlag, New York, 1995).

W. Leonard, Control of Electrical Drives (Springer Verlag, New York, 1990).

B. K. Bose, Modern Power Electronics and AC Drives (Prentice Hall of India, New Delhi, 2008).

A. Isidori, A. J. Krener, C. Gori-Giorgi, and S. Monaco, “Nonlinear decoupling via feedback: A differential-geometric approach,” IEEE Trans. Automatic Control, vol. 26, 1981, pp 331-345.

R. Marino, S. Peresada, and P. Valigi, “Adaptive input-output linearizing control of induction motors,” IEEE Trans. Automatic Control, vol.38, no.2, 1993, pp.208-221.

J. Chiasson, “Dynamic feedback linearization of the induction motor,” IEEE Trans. Auto. Control, vol. 38, no. 10, Oct. 1993, pp. 1588-1594.

A. Sabanovic and D. B. Izosimov, “Application of sliding modes to induction motor control,” IEEE Trans. Ind. App., vol.17, no.1, Jan 1981, pp. 41-49.

W.-J. Wang and J.-Y. Chen, “A new sliding mode position controller with adaptive load torque estimator for an induction motor,” IEEE Trans. on Energy Convn., vol. 14, no. 3, Sept 1999, pp. 413-418.

R. J Wai and W. K. Liu., “Nonlinear decoupled control for linear induction motor servo-drive using the sliding-mode technique,” IEE Proc. Control Theory Appln, vol. 148, no.3, May 2001, pp. 217–231.

J. Soltani, G.R.A. Markadeh, “A current-based output feedback mode control for speed sensorless induction machine drive using adaptive sliding mode flux observer,” Fifth PEDS Conf. 2003, 2003,vol.1, pp. 226-231.

K. B. Mohanty, Madhu Singh, “Robust control of a feedback linearized induction motor through sliding mode,” IEEE PEDES 2010 and Power India Conf. , Dec. 2010, New Delhi.

S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of robots by learning,” J. Robotic Systems, vol. 1, no. 2, 1984, pp. 123–140.

G. Casalino and G. Bartolini, “A learning procedure for the control of movements of robotic manipulators,” Proc. IASTED Symp. Robotics and Automation, 1984, Amsterdam, pp. 108–111.

T. Takagi, M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE. Trans Syst. Man Cybern, vol. 15, no.1, 1985, pp.116-132.

Mohanty, K.B., Singh, M., Performance improvement of induction motor drive using feedback linearization and fuzzy torque compensator with RTDS implementation, (2012) International Review of Electrical Engineering (IREE), 7 (3), pp. 4374-4382.

L. F. Pak, M. O. Faruque, X. Nie and V. Danivahi, “A versatile cluster based real-time digital simulator for power engineering research”, IEEE Trans on Power System, vol. 21, no. 2, May 2006, pp. 455-465.

C. Dufour, S. Abourida and J. Belanger, “Real-time simulation of induction motor IGBT drive on a pc-cluster”, Procc. of the Int. Conf. on Power System Transients, 2003, Hong Kong.

S. Meo, A. Perfetto, A Predictive Control of a DPWM Quasi Resonant Inverter Feeding Induction Motors, (2012) International Review on Modelling and Simulations (IREMOS), 5 (2), pp. 1122-1127.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize