The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorit...The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.展开更多
This paper presents a sliding mode observer for sensorless operation of SRM （switched reluctance motor） drive. Design of such an observer depends mainly on the nonlinear model of SRM. In this technique, neither extr...This paper presents a sliding mode observer for sensorless operation of SRM （switched reluctance motor） drive. Design of such an observer depends mainly on the nonlinear model of SRM. In this technique, neither extra hardware nor huge memory space are not required but it only requires active phase measurements. Furthermore, PI （proportional integral） and adaptive FLPI （fuzzy logic PI） controllers are suggested to operate individually along with the SMO （sliding mode observer） to cover a full speed range of sensorless controller. Both controller schemes operate in PWM （pulse width modulation） control mode. The proposed observer is implemented and tested using a digital signal processor. All results obtained with both simulation and experimental investigations corroborate the superior performance of the adaptive fuzzy logic controller （FLPI） when compared with those of PI controller.展开更多
文摘The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.
文摘This paper presents a sliding mode observer for sensorless operation of SRM （switched reluctance motor） drive. Design of such an observer depends mainly on the nonlinear model of SRM. In this technique, neither extra hardware nor huge memory space are not required but it only requires active phase measurements. Furthermore, PI （proportional integral） and adaptive FLPI （fuzzy logic PI） controllers are suggested to operate individually along with the SMO （sliding mode observer） to cover a full speed range of sensorless controller. Both controller schemes operate in PWM （pulse width modulation） control mode. The proposed observer is implemented and tested using a digital signal processor. All results obtained with both simulation and experimental investigations corroborate the superior performance of the adaptive fuzzy logic controller （FLPI） when compared with those of PI controller.
