• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.1
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• pp.29-34
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• 2005

The wound induction motor can provide high starting torque and reduced starting current simultaneously by inserting large resistor externally when starting. And this technique is one of the well known methods among the induction motor starting methods and generally used for heavy load starting such as crane and cement factories. The conventional PI controller has been widely used in industrial application due to the simple control algorithm and is generally used for control of current torque, position, and speed for the wound induction motor drive system. However, the conventional control system for wound induction motor may result in poor performance because sensors have to be used but are often limited by the environmental condition. Recently, to overcome these problems, many sensorless vector control methods for the wound induction motor have been studied. This paper presents a MRAS method for sensorless vector control of the wound induction motor drive. In the conventional MRAS method, in low frequency, the stator resistance variation may result in poor performance. Therefore, this paper presents a MRAS method with stator and rotor resistance tuning for sensorless vector control of the wound induction motor to overcome several shortages of the conventional MRAS caused by parameter variation and to enhance the robustness of the sensorless vector control. The validity and effectiveness of the proposed method is verified through digital simulation.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.307-312
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• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-FNN and estimation of speed using ANN controller The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.240-243
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• 2003

Sensorless Vector control of the permanent magnet synchronous motor(PMSM) typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. In this paper, by measuring the currents of the PMSM drive, a neural-network-based rotor position and speed estimation method for PMSM is described. Because the proposed estimator treats the estimated motor speed as the weights, it is possible to estimate motor speed to adapt back propagation algorithm with 2 layered neural network. The proposed control algorithm is applied to PMSM drive system. The operating characteristics controlled by neural networks control are examined in detail.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.1
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• pp.57-64
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• 2006

This paper is proposed for a artificial neural network(ANN) sensorless control based on the vector controlled induction motor drive, or proposes a adaptive fuzzy teaming control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed for a method of the estimation of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.12
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• pp.691-699
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• 2004

SRM(Switched Reluctance Motor) drives require the accurate position information of the rotor. These informations are generally provided by a tacho generator or digital shaft-position encoder These speed sensors lower the system reliability and require special attention to noise. This paper describes a new approach to estimating SRM speed from measured terminal voltages and currents for speed sensorless control. The described method is based on the sliding mode observer. The rotor speed and position observers are estimated by the adaptation law using the real and estimated currents. However, the conventional adaptive sliding mode observer based on the variable structure control theory has some disadvantages that the estimated values including the high-frequency chattering and the steady state error generated due to the infinite feedback gain chosen and the discontinuous control input. To reduce the chattering and steady state error, an integrator is also inserted in the sliding mode observer strategy. The described adaptive sliding mode observer decreases the vibration to the switching hyper-plane of the sliding mode by adding integrator. The described methodology incorporates the Lyapunov algorithm to drive the rotor speed and the stator resistance such that it can overcome the problem of sensitivity in the face of SRM parameter variation. Also, without any mechanical information. The rotor speed of SRM is obtained form adaptive scheme. The described method is verified through the simulation and experiment.

• Proceedings of the KIEE Conference
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• 2002.06a
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• pp.135-141
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• 2002

This paper presents an implementation of high-dynamic performance of position sensorless motion control system of Reluctance Synchronous Motor(RSM) drives for an industrial servo system with direct torque control(DTC), The problems of DTC for high-dynamic performance and maximum efficiency RSM drive due to a saturated stator linkage flux and nonlinear inductance curve with various load currents, The accurate estimation of the stator flux and torque are obtained using stator flux observer of which a saturated inductance Ld and Lq can be compensated by adapting from measurable the modulus and angle of the stator current space vector. To obtain fast torque response and maximum torque/current with varying load current, the reference command flux is ensured by imposing Ids=Iqs. This control strategy is proposed to fast response and optimal efficiency for RSM drive. In order to prove rightness of the suggested control algorithm, the actual experiment carried out at ${\pm}$20 and ${\pm}$1500 rpm. The developed digitally high-performance control system are shown some good response characteristic of control results and high performance features using 1.0kW RSM of which has 2.57 Ld/Lq salient ratio.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.157-164
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• 2000

This paper presents a method to detect a rotor position and to drive a BLDC motor from standstill to medium speed without any position sensor comparing the current responses due to the inductance variation in the rotor position. A rotor position at a standstill is identified by the current responses of six pulses injected to each phase of a motor. Once the motor stars up pulse train that is composed of long and short pulses is injected to the phase corresponding to produce the maximum torque and the next phase continuously. it provides not only the torque but also the information of the next commutation time effectively when the response of long and short pulses crosses each other after the same time delay. This method which is verified experimentally using a DSP can drive a BLDC motor to the medium speed smoothly without any rattling and time delay compared with the conventional sensorless algorithm.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.374-375
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• 2019

In this paper, In the whole industry, there is a tendency to replace brushless motors with brushless motors because of the high rate of failure in DC motors with brushes. Accordingly, many methods for driving a brushless motor have been developed and studied. In order to drive the brushless motor, it is essential to know the information about the rotor position of the motor. However, it is not possible to use a position sensor for rotor disconnection due to the structure of an electric compressor brushless DC motor. In this paper, we investigate the rotor position of the motor by using the counter electromotive force included in the voltage of the terminal made by Y connection by using the resistance of each phase without using Hall sensor or encoder generally used to detect the rotor position. A sensorless drive system for a square wave brushless direct current (DC) motor is proposed. To do this, we propose a method to detect the rotor position from the analyzed terminal voltage waveform by performing terminal voltage analysis of each phase for 3-phase, 2-exciton unipolar PWM.

• Journal of Power Electronics
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• v.6 no.1
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• pp.38-44
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• 2006

In this paper, a speed sensorless control for an ultrasonic motor (USM) using a neural network (NN) is presented. In the proposed method, rotor speed is estimated by a three-layer NN which adapts nonlinearities associated with load torque and motor temperature into control. The intrinsic properties of a USM, such as high torque for low speeds, high static torque, compact size, etc., offer great advantages for industrial applications. However, the speed property of a USM has strong nonlinear properties associated with motor temperature and load torque, which make accurate speed control difficult. These properties are considered in designing a control method through the application of mathematical models. In these strategies, a detailed speed model of the USM is required which makes actual applications impractical. In the proposed method, a three-layer NN estimates the speed of the USM from the drive frequency, the root mean square value of input voltage and the surface temperature of the USM, where no mechanical speed sensor is needed. The NN speed based estimator enables inclusion of variations in driving conditions due to input signals of the NN involved during the driving state of the USM. The disuse of sensors offers many advantages on both the cost and maintenance front. Moreover, the model free sensorless control method offers practical controller construction within a small number of parameters. To validate the proposed speed sensorless control method for a USM, experiments have been executed under several conditions.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.2
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• pp.189-195
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• 2005

This paper presents a new velocity estimation strategy for a non-salient permanent magnet synchronous motor drive without high frequency signal injection or special PWM pattern. This approach is based on the d-axis current regulator output voltage of the drive system, which contains the rotor position error information. The rotor velocity can be estimated through a rotor position tracking PI controller that controls the position error at zero. For zero and low speed operation, the PI gain of the rotor position tracking controller has a variable structure according to the estimated rotor velocity. Then, at zero speed, the rotor position and velocity have sluggish dynamics because the varying gains are very low in this region. In order to boost the bandwidth of the PI controller during zero speed, the loop recovery technique is applied to the control system. The PI tuning formulas are also derived by analyzing this control system by frequency domain specifications such as phase margin and bandwidth assignment.