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신경회로망을 이용한 불확실성을 갖는 유도전동기의 적응 백스테핑 속도제어기 설계
李殷昱(Eun-Wook Lee),鄭基澈(Kee-Chull Chung),李承學(Seung-Hak Lee) 대한전기학회 2006 전기학회논문지 D Vol.55 No.11
Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.
비선형 외란 관측기를 이용한 유도전동기의 강인 적응 백스테핑 제어
李殷昱(Eun-Wook Lee) 대한전기학회 2008 전기학회논문지 P Vol.57 No.2
In this paper, we propose a robust adaptive backstepping control of induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time-varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the fuzzy neural network(FNN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.