• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1527-1539
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• 2015

In this paper the hardware implementation of the direct torque control based on the fuzzy logic technique of induction motor on the Field-Programmable Gate Array (FPGA) is presented. Due to its complexity, the fuzzy logic technique implemented on a digital system like the DSP (Digital Signal Processor) and microcontroller is characterized by a calculating delay. This delay is due to the processing speed which depends on the system complexity. The limitation of these solutions is inevitable. To solve this problem, an alternative digital solution is used, based on the FPGA, which is characterized by a fast processing speed, to take the advantage of the performances of the fuzzy logic technique in spite of its complex computation. The Conventional Direct Torque Control (CDTC) of the induction machine faces problems, like the high stator flux, electromagnetic torque ripples, and stator current distortions. To overcome the CDTC problems many methods are used such as the space vector modulation which is sensitive to the parameters variations of the machine, the increase in the switches inverter number which increases the cost of the inverter, and the artificial intelligence. In this paper an intelligent technique based on the fuzzy logic is used because it is allows controlling the systems without knowing the mathematical model. Also, we use a new method based on the Xilinx system generator for the hardware implementation of Direct Torque Fuzzy Control (DTFC) on the FPGA. The simulation results of the DTFC are compared to those of the CDTC. The comparison results illustrate the reduction in the torque and stator flux ripples of the DTFC and show the Xilinx Virtex V FPGA performances in terms of execution time.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.3
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• pp.605-614
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• 1995

In the automatic transmission using planetary gear there exists nonlinearities due to the finite difference between gear ratios, which yield torque hole during shift and influence on the ride quality and life of clutch. Based on the reaction carrier and converter turbine speed sliding functions are defined. Nonlinear closed-loop control laws are derived using them. Computer simulation shows that the closed loop design is better than the open loop design and semi-closed loop design.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.359-364
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• 2006

This article describes the useful way to measure the torque and RPM of the geared motor. For this we have made the planetary geared reduction motor including 2 Hall sensors in it and the monitoring system. The monitoring system displays the sensing values (torque, rpm) and the calculated value (power) and it also has the network capability using the Bluetooth protocol. We will show that our solution is much more inexpensive and simple method to measure torque and rpm than before.

• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.409-410
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• 2012

Speed output has ripple such as periodic torque ripple if load torque with periodic ripple was injected in AC motor. In this paper, it is proposed method to reduce speed ripple through novel speed control method. It replaces algorithm to compensate torque ripple. Proposed method demonstrated through simulation using MATLAB SIMULINK.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.729-730
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neural network(A-FNN) controller and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled A-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the A-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.731-732
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• 2006

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
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• v.9 no.5
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• pp.679-690
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• 2009

The paper presents a new photovoltaic inverter for stand-alone induction motor application. The proposed system is composed of two stages. First stage is for the photovoltaic dc power feeding and second stage is dedicated to the motor-inverter subsystem and control technique. A direct torque control (DTC) with a novel switching strategy for motor torque ripple minimization is introduced. The novel DTC strategy is based on selecting a suitable voltage vector group for motor torque ripple minimization. A three-level voltage source inverter (VSI) is used instead of a two level inverter because the first has more available vectors and lower ripples in the output current and flux than the second, thus it has lower torque ripples. The photovoltaic array and battery bank are sized and the configuration is indicated based on sun-hour methodology. Simulation results show a comparison between three systems; two level VSI with conventional DTC strategy, three level VSI with conventional DTC, and the proposed system that has a novel DTC switching strategy applied to three level VSI. The results show that the proposed system has lower ripples in the current, flux and torque of the motor.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.78-89
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• 2012

The generated torque of a switched reluctance(SR) motor is highly nonlinear, which makes it difficult to determine the reference current commands for minimum torque ripples. In this paper, we present a computationally simple and efficient method to minimize torque ripples of SR motors based on iterative learning control. The reference current command of each phase minimizing torque ripples is identified in 2-dimensional look-up table form. Our learning control algorithm does not require the torque model, so our method is not affected by model errors and hence is very accurate. In order to justify our work, we present some computer simulation results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.9
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• pp.89-95
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• 2015

This paper presents a current sensorless maximum torque control of a synchronous reluctance motor drive. Stator current and current angle vs. maximum torque are obtained using measured d-axis and q-axis inductances. Then, d and q-axis stator voltage references for current sensorless maximum torque control are calculated from stator voltage equations. The proposed method is verified by simulation results comparing with conventional method.