Robust backstepping control of induction motor drives using artificial neural networks and sliding-mode flux observers
Yazdanpanah, R. and Soltani, J. (2007) Robust backstepping control of induction motor drives using artificial neural networks and sliding-mode flux observers. International Journal of Engineering, Transactions A: Basics, 20 (3). pp. 221-232. ISSN 1728-1431
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Abstract
In this paper, using the three-phase induction motor fifth order model in a stationary two axis reference frame with stator current and rotor flux as state variables, a conventional backstepping controller is first designed for speed and rotor flux control of an induction motor drive. Then in order to make the control system stable and robust against all electromechanical parameter uncertainties as well as to the unknown load torque disturbance, the backstepping control is combined with artificial neural networks in order to design a robust nonlinear controller. It will be shown that the composite controller is capable of compensating the parameters variations and rejecting the external load torque disturbance. The overall system stability is proved by the Lyapunov theory. It is also shown that the method of artificial neural network training, guarantees the boundedness of errors and artificial neural network weights. Furthermore, in order to make the drive system free from flux sensor, a slidingmode rotor flux observer is employed that is also robust to all electrical parameter uncertainties and variations. Finally, the validity and effectiveness of the proposed controller is verified by computer simulation.
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Item type: Article ID code: 90160 Dates: DateEventOctober 2007PublishedSubjects: Technology > Engineering (General). Civil engineering (General) > Bioengineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 07 Aug 2024 10:39 Last modified: 08 Aug 2024 02:06 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90160