A. Merabet, M. Ouhrouche, and R.T. Bui


  1. [1] J. Chiasson, Dynamic feedback linearization of the induction motor, IEEE Trans. on Automatic Control, 38 (10), 1993, 1588–1594. doi:10.1109/9.241583
  2. [2] T.K. Boukas & T.G. Habetler, High-performance induction motor speed control using exact feedback linearization with state und state derivative feedback, IEEE Trans. on Power Electronics, 19 (4), 2004, 1022–1028. doi:10.1109/TPEL.2004.830042
  3. [3] M.K. Maaziz, P. Mendes, & P. Boucher, A new multivariable control strategy of induction motors, Control Engineering Practice, 10, 2002, 605–613. doi:10.1016/S0967-0661(02)00012-6
  4. [4] F. Chen & M.W. Dunnigan, A new non-linear siding-mode torque and flux control method for an induction machine incorporating a sliding-mode flux observer, International Journal of Robust and Nonlinear Control, 14, 2004, 463–486. doi:10.1002/rnc.881
  5. [5] C.E. Garcia, D.M. Prett, & M. Morari, Model predictive control: Theory and practice: A survey, Automatica, 3, 1989, 335–348. doi:10.1016/0005-1098(89)90002-2
  6. [6] D.W. Clarke, C. Mohtadi, & P.C. Tuffs, Generalized predictive control, Part 1: The basic algorithm, Automatica, 23, 1987, 137–148.
  7. [7] D.W. Clarke, C. Mohtadi, & P.C. Tuffs, Generalized predictive control, Part 2: The basic algorithm, Automatica, 23, 1987, 149–163.
  8. [8] D. Soloway & P.J. Haley, Neural generalized predictive control: A Newton-Raphson implementation, Proc. 11th IEEE Int. Symp. on Intelligent Control, Dearborn, MI, 1996, 277–282. 150
  9. [9] R. Kennel, A. Linder, & M. Linke, Generalized predictive control (GPC) ready for use in drive applications?, 32nd IEEE Power Electronics Specialists Conf. (PESC), Vancouver, 2001, 17–22.
  10. [10] M.K. Maaziz, P. Boucher, & D. Dumer, A new control strategy for induction motor based on non-linear predictive control and feedback linearization, International Journal of Adaptive Control and Signal Processing, 14, 2000, 313–329. doi:10.1002/(SICI)1099-1115(200003/05)14:2/3<313::AID-ACS589>3.0.CO;2-D
  11. [11] R. Hedjar, R. Toumi, P. Boucher, & D. Dumer, Two cascaded nonlinear predictive controls of induction motor, IEEE Conf. on Control Application, 1, Istanbul, 2003, 458–463.
  12. [12] M. Fliess, J. Lévine, J. Martin, & P. Rouchon, Flatness and defect of non-linear systems: Introductory theory and examples, International Journal of Control, 61, 1995, 1327– 1361. doi:10.1080/00207179508921959
  13. [13] P. Martin & P. Rouchon, Two remarks on induction motors, Proc. CESA, Lille, France, 1996, 76–79.
  14. [14] E. Delaleau, J.P. Louis, & R. Ortega, Modeling and control of induction motors, International Journal of Applied Mathematics and Computer Science, 11 (1), 2001, 105–129.
  15. [15] P. Vas, Artificial-intelligence-based electrical machines and drives: Application of fuzzy, neural, fuzzy neural and genetic algorithm-based techniques (New York: Oxford University Press, 1999).
  16. [16] D. Neumerkel, J. Franz, L. Krüger, & A. Hidiroglu, Real time application of neural model predictive control for an induction servo drive, Proc. 3rd IEEE Conf. on Control Applications, Glasgow, 1994, 433–438.
  17. [17] M. Cirrincione & M. Pucci, An MRAS-based sensorless high performance induction motor drive with a predictive adaptive model, IEEE Trans. on Industrial Electronics, 52 (2), 2005, 532–551. doi:10.1109/TIE.2005.844247
  18. [18] M.M. Negm, A.H. Mantawy, & M.H. Shwehdi, A global ANN algorithm for induction motor based on optimal preview control theory, Iranian Journal of Electrical and Computer Engineering, 2 (1), 2003, 23–29.
  19. [19] L. Krüger, D. Naunin, & C. Garbrecht, Stochastic and neural models of an induction motor, Mathematics and Computers in Simulation, 46, 1998, 313–324. doi:10.1016/S0378-4754(97)00144-4
  20. [20] L. Constant, P. Lagarrigues, B. Dagues, I. Rivals, & L. Personnaz, Modeling of electromechanical systems using neural networks, in P.S. Szczepaniak (ed.), Computational intelligence and applications (Physica-Verlag, 1999).
  21. [21] J.F. Martins, A.J. Pires, & J.A. Dente, Automatic input/output modeling of a squirrel-cage induction motor drive system using neural network, EPE’97, 4, Trondheim, Norway, 1997, 632– 637.
  22. [22] I.H. Kim, S. Fok, K. Fregene, D.H. Lee, T.S. Oh, & W.I. Wang, Neural network-based system identification and controller synthesis for an industrial sewing machine, International Journal of Control, Automation, and Systems, 2 (1), 2004, 83–91.
  23. [23] L. Boullart, A. Krijgsman, & R.A. Vingerhoeds, Application of artificial intelligence in process control (Oxford, UK: Pergamon Press, 1992).
  24. [24] M.H. Hassoun, Fundamentals of artificial neural networks (Cambridge, MA: MIT Press, 1995).
  25. [25] P.H. Sørensen, M. Nørgaard, O. Ravn, & N.K. Poulsen, Implementation of neural network based non-linear predictive control, Neurocomputing, 28, 1999, 37–51. doi:10.1016/S0925-2312(98)00114-3
  26. [26] M. Lazar & O. Pastravanu, A neural predictive controller for non-linear systems, Mathematics and Computers in Simulation, 60, 2002, 315–324. doi:10.1016/S0378-4754(02)00023-X
  27. [27] M. Ouhrouche, Estimation of speed, rotor flux and rotor resistance in cage induction motor sensorless drive using the EKF algorithm, International Journal of Power and Energy Systems, 22 (2), 2002, 103–109. Appendix A: List of Symbols ω Rotor speed φrα , φrβ (α, β) components of the rotor flux space vector isα, isβ (α, β) components of the stator current space vector usα, usβ (α, β) components of the stator voltage space vector p Number of pole pairs Rs, Rr Stator and rotor resistances Ls, Lr, Lm Stator, rotor, and magnetizing inductances τr = Lr/Rr Rotor electrical time constant σ = (1 − L2 m/LsLr) Leakage factor J Rotor moment of inertia (kg·m2 ) B Damping coefficient (N·m·s) Tl Load torque (N·m) Appendix B: Induction Machine Parameters Rated speed ωnom 150 rad/s Rated torque Tlnom 0.38 N·m Number of pole pairs p 2 Stator resistance Rs 4.287 Ω Rotor resistance Rr 2.610 Ω Stator inductance Ls 0.404 H Rotor inductance Lr 0.398 H Magnetizing inductance Lm 0.368 H Rotor moment of inertia J 0.025 Kg·m2 151

Important Links:

Go Back