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2002 | 12 | 2 | 221-233
Tytuł artykułu

Fuzzy and neural control of an induction motor

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.
Rocznik
Tom
12
Numer
2
Strony
221-233
Opis fizyczny
Daty
wydano
2002
otrzymano
2001-06-06
poprawiono
2001-09-10
(nieznana)
2001-11-18
Twórcy
  • University of Science and Technology of Oran, Faculty of Electrical Engineering, B.P. 1505 El Mnaouar, Oran 31 000, Algeria
  • University of Science and Technology of Oran, Faculty of Electrical Engineering, B.P. 1505 El Mnaouar, Oran 31 000, Algeria
Bibliografia
  • Brdyś M.A. and Kulawski G.J. (1999): Dynamic neural controllers for induction motor. - IEEE Trans. Neur. Netw., Vol. 10, No. 2, pp. 340-355.
  • Cao S.G., Rees N.W. and Feng G. (1999): Analysis and design of fuzzy control systems using dynamic fuzzy state space models. - IEEE Trans. Fuzzy Syst., Vol. 7, No. 2, pp. 192-199.
  • Chan C.C. and Wangs H. (1990): An effective method for rotor time constant identification for high performance induction motor vector control. - IEEE Trans. Indust. Electr., Vol. 37, No. 6, pp. 477-482.
  • Chen C-Li and Chang M-Hui (1998): Optimal design of fuzzy sliding mode control: A comparative study. - Fuzzy Sets Syst., Vol. 93, pp. 37-48.
  • Chin T., Miyashita I. and Koga T. (1996): Sensorless induction motor drive: An innovative component of advanced motion control. - Proc. IFAC 13-th World Congress, San-Francisco, USA.
  • Elloumi M., Al-Hamadi A. and Ben-Brahim L. (1998): Survey of speed sensorless controls of induction motor drive. - Proc. IEEEIECON'98 Conf. Record, Aachen, Germany, pp. 1018-1023.
  • Hung J.Y., Gao W. and Hung J.C. (1993): Variable structure control: A survey. - IEEE Trans. Industr. Electr., Vol. 40, No. 1, pp. 2-21.
  • Hunt K.J. and Sbarbaro D. (1991): Neural networks for non-linear model control. - IEE Proc., Part D, Vol. 138, pp. 431-438.
  • Hunt K.J., Sbarbaro D., Zbikowski R. and Gawthrop P.J. (1992): Neural networks for control systems: A survey. - Automatica, Vol. 28, pp. 1083-1112.
  • Kawaji S. and Matsunaga N. (1994): Fuzzy control of VSS type and its robustness, In: Fuzzy Control Systems (A. Kandel and G. Langholz, Eds.). - Boca Raton, pp. 226-242.
  • Kim Y.H., Kim S.S. and Hong I.P. (1998): Speed sensorless vector control of high speed induction motor using intelligent control algorithm. - Proc. IEEEIECON'98 Conf. Record, Aachen, Germany, pp. 888-892.
  • Kung Y.S., Liaw C.M. and Ouyang M.S. (1995): Adaptive speed control for induction motor drives using neural networks. - IEEE Trans. Industr. Electr., Vol. 42, No. 1, pp. 25-32.
  • Kwan C.M. and Lewis F.L. (2000): Robust backstepping control of induction motors using neural networks. - IEEE Trans. Neur. Netw., Vol. 11, No. 5, pp. 1178-1187.
  • Lee C.C. (1990): Fuzzy logic in control systems: Fuzzy logic controller- Parts I and II. - IEEE Trans. Syst. Man Cybern., Vol. 20, No. 2, pp. 404-435.
  • Mei F., Zhihong M., Yu X. and Nguyen T. (1998): A robust tracking control scheme for a class of non-linear Systems with fuzzy nominal models. - Appl. Math. Comp. Sci., Vol. 8, No. 1, pp. 145-158.
  • Morari M. and E. Zafiriou E. (1989): Robust Process Control. - Englewood Cliffs, NJ: Prentice Hall.
  • Palm R. (1994): Robust control by fuzzy sliding mode. - Automatica, Vol. 30, pp. 1429-1437.
  • Shaw A. and Doyle F. (1997): Multivariable non-linear control application for a high purity distillation column using a recurrent dynamic neuron model. - J. Process Contr., Vol. 7, No. 4, pp. 255-268.
  • Si J. and Zhou G. (1996): A reduced memory Levenberg-Marquardt algorithm. - Proc. 13-th IFAC World Congress, San Francisco, USA, pp. 233-236.
  • Tajima H. (1993): Speed sensorless field orientation control of induction motor. - IEEE Trans. Industr. Applic., Vol. 29, No. 1, pp. 175-181.
  • Trzynadlowski A.M. (1994): The Field Orientation Principle in Control of Induction Motors. - Dordrecht: Kluwer.
  • Umanand L. and Bhat S.R. (1994): Adaptation of the rotor time constant for variation in rotor resistance of induction motor. - Proc. IEEE Annual Meeting, Denver, pp. 738-743.
  • Vas P. (1990): Vector Control of AC Machines. - London: Oxford University Press.
  • Zhen L. and Xu L. (1998): Sensorless field orientation control of induction machines based on mutual MRAS scheme. - IEEE Trans. Industr. Electr., Vol. 45, No. 5, pp. 824-830.
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv12i2p221bwm
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