• Journal of Advanced Marine Engineering and Technology
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• v.26 no.6
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• pp.695-704
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• 2002

Generally, induction motor controller requires rotor speed sensor for commutation and current control, but it increases cost and size of the motor. So in these days, various researches including speed sensorless vector control have been reported and some of them have been put to practical use. In this paper a new speed estimation method using neural networks is proposed. The optimal neural network structure was tracked down by trial and error, and it was found that the 8-16-1 neural network has given correct results for the instantaneous rotor speed. Supervised learning methods, through which the neural network is trained to learn the input/output pattern presented, are typically used. The back-propagation technique is used to adjust the neural network weights during training. The rotor speed is calculated by weights and eight inputs to the neural network. Also, the proposed method has advantages such as the independency on machine parameters, the insensitivity to the load condition, and the stability in the low speed operation.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.694-699
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• 2012

This paper proposes a new sensorless speed control scheme of permanent magnet DC motor using a numerical model and hysteresis controller, which requires neither shaft encoder, speed estimator nor PI controllers. By supplying the identical instantaneous voltage to both model and motor in the direction of reducing torque difference, the rotor speed approaches to the model speed, namely setting value and the system can control motor speed precisely. As the numerical model whose electric parameters are the same as those of the actual motor is adopted, the armature rotating speed can be converged to the setting value by controlling torque on both sides to be equalized. And the hysteresis controller controls torque by restricting the torque errors within respective hysteresis bands, and motor torque are controlled by the armature voltage. The experiment results indicate good speed and load responses from the low speed range to the high, show accurate speed changing performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.998-1004
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• 2016

This paper investigates a new sensorless control appling a virtual flux oriented vector control without the line voltage sensor for the power factor correction of 3 phase PWM converter. The DC output voltage is controlled by applying the kalman filter algorithm for the virtual flux estimation and the synchronous phase is obtained by using the estimated virtual flux equation based on kalman filter. This method is used to reduce the calculation time of the system and obtain a stable control that the input current including the harmonics and the noise is improved. The proposed system implement PFC algorithm in the variable region of DC output voltage. It can obtain the unity power factor, and can precisely control the DC output voltage in the load variation and in the variable voltage range. The performance of the proposed algorithm is verified through simulation and experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1743-1750
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• 2003

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as a speed detector, but they increase cost and size of the motor and restrict the industrial drive applications. So in these days, many papers have reported in the sensorless operation of DC motor〔3­5〕. This paper presents a new sensorless strategy using neural networks〔6­8〕. Neural network has three layers which are input layer, hidden layer and output layer. The optimal neural network structure was tracked down by trial and error, and it was found that 4­16­1 neural network structure has given suitable results for the instantaneous rotor speed. Also, learning method is very important in neural network. Supervised learning methods〔8〕 are typically used to train the neural network for learning the input/output pattern presented. The back­propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.635-643
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• 2018

This paper propose a method to identify the motor parameters and improve input voltage error which affect the low speed position error of the back-emf(back electromotive force) based sensorless algorithm and to secure the operation reliability and stability even in the case where the load fluctuation is severe and the start and low speed operation frequently occurs. In the model-based observer used in this paper, stator resistance, inductance, and input voltage are particularly influential factors on low speed performance. Stator resistance can cause resistance value fluctuation which may occur in mass production process, and fluctuation of resistance value due to heat generated during operation. The inductance is influenced by the fluctuation due to the manufacturing dispersion and at a low speed where the change of the current is severe. In order to find stator resistance and inductance which have different initial values and fluctuate during operation and have a large influence on sensorless performance at low speed, they are commonly measured through 2-point calculation method by 2-step align current injection. The effect of voltage error is minimized by offsetting the voltage error. In addition, when the command voltage is used, it is difficult to estimate the back-emf due to the relatively large distortion voltage due to the dead time and the voltage drop of the power device. In this paper, we propose a simple circuit and method to detect the voltage by measuring the PWM(Pulse Width Modulation) pulse width and compensate the voltage drop of the power device with the table, thereby minimizing the position error due to the exact estimation of the back-emf at low speed. The suitability of the proposed algorithm is verified through experiment.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2077-2078
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• 2011

Now a days, the induction motor is widely used in industry automation. Without monitoring the motor fault, maintenance cost is increased undesirably high. The slip frequency is included in the feature frequency, so rotating rotor speed is needed. In this paper, a sensorless motor speed estimation method, rotor slot harmonic(RSH) method is suggested and a solution of rotor bar diagnosis is proposed for motor running with light-load. When the rotor is rotating, it shows the harmonic signal of back-emf voltage related with number of rotor slot. So from the power spectrum of current signal, we can find the rotor speed.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.2015-2017
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• 1998

This paper deals with the design of a field oriented control system model for the high performance induction motor using Matlab with Simulink. The proposed control system model, which is not used the speed and flux sensor, contains IM model, Tranformation, Decoupling, FFOC(Field Flux Orientation Controller), Torque calculator and PI Controller to control speed, torque. Results present the stator and rotor flux phasor trajectory, the startup and transient response of speed, torque and stator current with field oriented control and the response to changes in reference speed with no load. This paper shows that the propose control system is more robust than other vector control system, and suggest the enchanced model, using Matlab with Simulink for the high performance in induction motor control.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.5
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• pp.472-481
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• 2002

In this paper, the instantaneous flux Is applied to control the position of the SRG (Switched Reluctance Generator) without position sensor. The position information of the rotor is required in the drive of SRG. These data are generally obtained by a shaft encoder or resolver. In some cases, the EMI(Electro Magnetic Interference), vibration, thermal, and humidity environments may cause the difficulties in maintaining the satisfactory performance for the position detection. Therefore, the elimination of the position and speed sensor is needed. In this paper, a new method for the position estimation of the SRG is proposed. The estimation of the flux is calculated by using the measured voltage and current. The rotor position gets from the flux profile. The output voltage is also controlled constantly by PR control algorithm. These methods are verified by computer simulations md experiments using DSP. Experimental results certificate that the proposed method is able to control the SRG stable, and keep the output voltage constant in spite of changing of the load.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.2
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• pp.91-102
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• 1999

The paper describes a robust adaptive control algorithm for induction motor drive without speed sensor at low speed range. The control algorithm use only current sensors in a space vector pulse width modulation within loop control with rotor speed estimation and voltage source inverter. On-line rotor speed estimation is based on utilizing parallel model reference adaptive control system. MRAC of the modified flux model for flux and rotor speed estimator uses dual-adaptation mechanism, ${\omega}_r$ and ${\omega}_e$ scheme. The estimated flux components in the model can be compensated from the effects of offset errors on pure integrals. It can be compensated to the parameter variations and torque fluctuation with speed estimation in less then 10 rad/sec. In a simulation, the proposed induction motor control algorithm without speed sensor at very low speed range are shown to operate very well in spite of variable rotor time constant and fluctuating load without change the controller parameters. The suggested control strategy and estimation method have been validated by simulation study, and it proposed the designed system for the implementation using TI320C31 DSP/ASIC controller.