• Journal of Power Electronics
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• 제16권1호
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• pp.1-8
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• 2016

Dead time is typically incorporated in voltage source inverter systems to prevent short circuit cases. However, dead time causes an error between the output voltage and reference voltage. Hence, voltage equation-based algorithms, such as motor parameter estimation and back electromotive force (EMF)-based sensorless algorithms, are prone to estimation errors. Several dead-time compensation methods have been developed to reduce output voltage errors. However, voltage errors are still common in zero current crossing areas, and an effect of the error is much worse in a low speed region. Therefore, employing voltage equation-based algorithms in low speed regions is difficult. This study analyzes the conventional dead-time compensation method and output voltage errors in low speed operation areas. A current shaping method that can reduce output voltage errors is also proposed. Experimental results prove that the proposed method reduces voltage errors and improves the accuracy of the parameter estimation method and the performance of the back EMF-based sensorless algorithm.

• 수산해양교육연구
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• 제7권1호
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• pp.127-132
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• 1995

In this paper, a new speed estimation method of induction motors based on the very quick torque control is proposed to realize speed sensorless control. The proposed method can be realized very simply by detecting primary motor current and voltage command at every sampling time. As the method need not the differential value of primary current in a arithmetic of voltage command, it can be expected to promote the precision of speed estimation in low speed area, especially. Through the numerical simulation, the validity of the proposed method was successfully confirmed.

• 전기학회논문지P
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• 제55권3호
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• pp.117-122
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• 2006

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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 F
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• pp.2088-2090
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• 1997

This paper consists of the speed sensorless vector control of induction motors with the estimation of rotor resistance. In the application of variable-speed induction motor drives, if an inaccurate rotor resistance is used because the rotor resistance can change due to skin effects and temperature variables, it is difficult to achieve a collect field orientation. In this paper, to overcome these difficulties adaptive algorithm is designed for rotor resistance identification. The proposed adaptive algorithm for rotor resistance estimation in the synchronous reference frame is applied by sliding mode current controller satisfing persistent excitation(PE) condition. Adaptive flux observer is here used for the purpose of estimating rotor flux and speed in the speed sensorless scheme. Computer simulations are carried out to verify the validity of the proposed algorithm.

• 제어로봇시스템학회논문지
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• 제11권7호
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• pp.565-571
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• 2005

We present a novel speed estimation of a Permanent Magnet Synchronous Motor(PMSM) based on Support Vector Regression(SVR). The proposed method can estimate wide speed range, including 0.33Hz with full load, accurately in the steady and transient states where motor parameters variations are known without parameter estimator. Moreover, the method does not need offline training previously but is trained on-line. The training starts with the PMSM operation simultaneously and estimates the speed in real time. The experimental results shows the validity and the usefulness of the proposed algorithm for the 0.4Kw PMSM DSP(TMS320VC33) drive system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.216-218
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• 2006

The paper is proposed high performance control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper is proposed speed control of induction motor using AFLC and estimation of speed using artificial neural network(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 AFLC and ANN controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제5B권4호
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• pp.358-365
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• 2005

In induction machine drive without a speed sensor, the estimation of the motor flux and speed often becomes deteriorated at low speeds with low back EMF. Our analysis shows that, in addition to the state resistance variation, the estimated value of field orientation angle is often corrupted by accumulative errors from the integration of voltage variables at motor terminals that have low signal/noise ratio at low frequencies. A repetitive loop path of integration in the feedback can amplify this type of error, thus speeding up the degradation process. The control system runs into information starvation due to the loss of correct field orientation. The machine's spiral vectors are controlled only in a reduced dimension in this situation. A novel control scheme is developed to improve the control performance of motor's current, torque and speed at low frequencies. The scheme gains a full-dimensional vector control and is less sensitive to the combined effect of the error sources at the low frequencies. Experimental tests demonstrate promising performances are achievable even below 0.5 Hz.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1013-1016
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• 2000

In this study, the estimate speed of rotor in the induction motor with Model Reference Adaptive control System (MARC) principle and to study that vector control system feedbacks speed estimated to speed control system and its result is as follows; Considering with explanation an influence of speed estimation mechanism depend on error about the second resistance size established, it estimates the deviation of the second resistance establishment and exhibits a compensation method, what is more, it designs a reparation program using the fuzzy algorithm and testifies the result with the computer simulation. And besides, it composes the load torque estimation and estimates the load torque, as the result, feedback-compensating the result of estimation, it improves the efficiency. In consequence, it makes a good result for more powerful vector control system about the outside trouble.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.255-257
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• 1995

In this paper, a new speed estimation method of induction motors based on the very quick torque control is proposed to realize speed sensorless control. The proposed method can be realized very simply by detecting primary motor current and voltage command at every sampling time. As the method need not the differential value of primary current in a arithmetic of voltage command, it can be expected to promote the precision of speed estimation in low speed area, especially. Through the numerical simulation, the validity of the proposed method was successfully confirmed.

• Journal of Power Electronics
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• 제17권1호
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• pp.149-160
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• 2017

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.