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In this paper, because the induction machines are described as the plants of highly nonlinear and parameters time-varying, in order to obtain a very well control performances that a conventional model reference adaptive inverse control (MRAIC) can not be gotten, a fuzzy neural network-based model reference adaptive inverse control strategy for induction motors is presented based on the rotor field...
In this paper, based on the radial basis function (RBF) neural network, a fuzzy predictive current control strategy for the doubly fed machine (DFM) is presented. The dynamic model of voltage, flux linkage, electromagnetic torque and mechanical motion equation for DFM are expressed. Because the DFM structure is complex and the DFM parameters are variable according to the operating conditions and environments,...
In this paper, based on a radial basis function (RBF) neural network, a sliding mode control (SMC) strategy for brushless doubly fed machine (BDFM) is presented. The operating principle of BDFM has been introduced. The dynamic model of rotor field oriented and electromagnetic torque for BDFM is expressed. The proposed controller for BDFM eliminates the chattering encountered by most SMC schemes, and...
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