Takagi-Sugeno Fuzzy Control of Induction Motor

This paper deals with the synthesis of fuzzy state feedback controller of induction motor with optimal performance. First, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate a non linear system in the synchronous d-q frame rotating with electromagnetic field-oriented. Next, a fuzzy controller is designed to stabilise the induction motor and guaranteed a minimum disturbance attenuation level for the closed-loop system. The gains of fuzzy control are obtained by solving a set of Linear Matrix Inequality (LMI). Finally, simulation results are given to demonstrate the controller-s effectiveness.





References:
[1] F.Betin, D.Depernet, P.Floczek, A.Faqir, D.Pinchon, and C.Goeldel.
Control of induction machine drive using fuzzy logic approach: Robustness
study. In Industrial Electronics, 2002. ISIE 2002. Proceedings
of the 2002 IEEE International Symposium on, volume 2,8-11, pages
361-366, July 2002.
[2] X.Ding, Q.Liu, Xiaona.Ma, Xiaoran.He, and Qing Hu. The fuzzy direct
torque control of induction motor based on space vector modolation. In
Third International Conference on Natural Computation (ICNC), 2007.
[3] Youssef H. A and Wahba M.A. Adaptive fuzzy mimo control of
induction motors. Expert systems with applications, 2008.
[4] Ibrahim Z and Levi. A comparative analysis of fuzzy logic and pi speed
control in high performance ac drives using experimental approach.
IEEE, Trans. Ind. APPL, 38(5):1210-1218, 2002.
[5] H. Rehman and R. Dhaouadi. A fuzzy learning-sliding mode controller
for direct field-oriented induction machinese. Neurocomputing, 71:2693-
2701, 2008.
[6] R.Marino, P. Tomei, and C.M. Verrelli. A global tracking control for
speed-sensorless inductions motors. Automatica, 40:1071-1077, 2004.
[7] R.Marino, S.Peresada, and P. Valigi. Adaptative input-output linearizing
control of inductions motors ", 38(2), 208-221. IEEE Transactions on
Automatic control, 38(2):208-221, 1999.
[8] R.Marino, S. Peresada, and P. Tomei. Global adaptative output feedback
control of inductions motors with uncertain rotor resistance. IEEE
Transactions on Automatic control, 44(5):967-983, 1993.
[9] R.Marino, S. Peresada, and P. Tomei. adaptative output feedback control
of current-fed induction motors with uncertain rotor resistance and load
torque. Automatica, 34(5):917-624, 1998.
[10] C.Duval, G. Clerc, and Y. Le Gorrec. Induction machine control using
robust eigenstructure assignment. Control Engineering Practice, 14:29-
43, 2006.
[11] Bor sen Chen, Chung-Shi Tseng, and Huey-Jian Uang. Robustness
design of non linear dynamic systems via fuzzy linear control. IEEE
Transaction on fuzzy control, 7(5):Sen1999, 1999.
[12] Kazuo.T, Shigeki.H, and H. O. Wang. Multiobjective control of a vehicle
with triple trailers. IEEE Transaction on mechatronics, 7(3), 2002.
[13] M.Kachaou, M.Souissi, and A.Toumi. H∞ guaranteed cost fuzzy
control for non-linear systems: An lmis approach. Studies in Informatics
and Control, 14(4), 2005.
[14] Khiar D. Robust takagi-sugeno fuzzy control of a spark ignition engine.
Control Engineering Practice, 2007.
[15] T.Taniguchi, K. Tanaka, K. Yamafuji, and H.O. Wang. A new pdc
for fuzzy reference models. In IEEE International Fuzzy Systems
Conference Proceedings, Seoul, Korea., August 22-25 1999.
[16] A. El Hajjaji and S. Bentalba. Fuzzy path tracking control for automatic
steering of vehicles" 43 (2003) 2003-213. Robotics and Autonomous
systems, 43:203-213, 2003.
[17] T.M.Guerra and J.Lauber. Control laws for takagi-sugeno fuzzy models.
Fuzzy sets and systems, 120:95-108, 2001.
[18] A. El Hajjaji, M. Chadli, and G .L. Reyes. Commande bas'ee sur la
mod'elisation floue de type takagi-sugeno d-un proc'ed'e exp'erimental `a
quatre cuves. In CIFA, 2008.
[19] Tseng C, Bor sen Chen, and Uang H.J. Fuzzy tracking control design
for non linear dynamic system via ts fuzzy model. IEEE Trans fuzzy
system, 9:381-392, 2001.
[20] F. Zheng, Q-G. Wang, and T. H. Lee. Output tracking control of mimo
fuzzy nonlinear systems using variable structure control approach. IEEE
Trans. Fuzzy system, 10(6), 2002.
[21] Chong. Lin, Q-G. Wang, and T. H. Lee. Output tracking control
for nonlinear via t-s fuzzy model approach. IEEE Trans. systems.
Cybernetics, 36(2), 2006.