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Using an adaptive fuzzy-logic system to optimize the performances and the reduction of chattering phenomenon in the control of induction motor

Barazane Linda (Faculty of Electronic & Computing, University of Science and Technology Houari, Boumediene (USTHB), Algiers, Algeria)
Khwaldeh Ali (Department of computer sciences Engineering Faculty of Engineering, Philadelphia University, Jordan)
Krishan Suboh Mohammed Monzer (Al-Balq'a Applied University, Engineering of Information and Measurement Systems, Jordan)
Sicard Pierre (Groupe de Recherche en Électronique Industrielle, Université du Québec à Trois-Rivières C.P., Trois- Rivières (Québec) Canada)

Neural networks and fuzzy inference systems are becoming well recognized tools of designing an identifier/controller capable of perceiving the operating environment and imitating a human operator with high performance. Also, by combining these two features, more versatile and robust models, called 'neuro-fuzzy' architectures have been developed. The motivation behind the use of neuro-fuzzy approaches is based on the complexity of real life systems, ambiguities on sensory information or time-varying nature of the system under investigation. In this way, the present contribution concerns the application of neuro-fuzzy approach in order to perform the responses of the speed regulation and to reduce the chattering phenomenon introduced by sliding mode control, which is very harmful to the actuators in our case and may excite the unmodeled dynamics of the system. The type of the neuro-fuzzy system used here is called:' adaptive neuro fuzzy inference controller (ANFIS)'. This neuro-fuzzy is destined to replace the speed fuzzy sliding mode controller after its training process. Simulation results reveal some very interesting features. .

Keywords: nonlinear feedback control, sliding mode control, adaptive neuro fuzzy inference system, ANFIS, induction motor