• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.149-152
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• 2004

Speed and torque control of PMSM(Permanent Magnet Synchronous Motor) are usually achieved by using position and speed sensors which require additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been performed for the elimination of speed and position sensors. In this paper, a novel speed sensorless control of a permanent magnet synchronous motor based on SVMR(Support Vector Machine Regression) is presented. The SVM regression method is an algorithm that estimates an unknown mapping between a system's input and outputs, from the available data or training data. Two well-known different voltage model is necessary to estimate the speed of a PMSM. The validity and the usefulness of proposed algorithm are thoroughly verified through numerical simulation.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.3
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• pp.124-130
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• 1999

Speed and position sensors require the additional mounting space, reduce the reliability in harsh environments and increase the cost of motor. Various control algorithms have been proposed for the elimination of speed senor. This paper investigates a novel speed sensorless control of induction motor. The proposed control strategy is based on MRAS(Model Reference Adaptive System) using state observer as a reference model for flux estimation. This algorithm may overcome several shortages of conventional MRAS: integrator problems, small EMF at low speed and relatively large sensitivity to resistance variation. The proposed algorithm is verified through simulation and experiment.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.510-513
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• 1996

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 at the beginning of the transient state. And an adaptive flux observer is used for the purpose of estimating rotor flux and speed in the speed sensorless scheme. Computer simulations are carried out to verity the validity of the proposed algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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• pp.30-37
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• 2011

Recently, SynRM has been focused by many researchers and there has been a lot of works for the industrial application of SynRM. In spite of several merits of SynRM, the information of exact rotor position is also required to perform the precise torque control, which causes the increment of cost and demerits SynRM to use in industrial application. Therefore, we studied sensorless control algorithm for the torque control of SynRM to overcome the demerits. Specially we proposed simple algorithm to estimate rotor position using trigonometric function, verified with computer simulation and experiment.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.3
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• pp.249-253
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• 2016

In this study, a harmonic-pulsation-compensator (HPC) is presented to reduce a periodic speed ripple in IPMSM. A proportional-integral compensator in HPC is proposed instead of the existing integral compensator to reduce the speed ripple more rapidly. A formula to calculate a rotation angle is also proposed, making compensation optimal in sensored and sensorless controls. The validity of the proposed algorithm is verified by experiments.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.107-113
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• 2002

To monitor the torque of an induction motor using current, rotating speed has been measured and used to calculate the slip angular velocity. Additional sensor, however, can cause extra expense and trouble. In this paper, a new algorithm is proposed to monitor the torque of vector controlled induction motor without any speed measuring sensor. Only stator current is measured to estimate the magnetizing current which is used to calculate flux linkage, rotor velocity and motor torque. Graphical programming is used to implement the suggested algorithm and to monitor the torque of an induction motor in real time. To solve the fluctuation problem of estimated torque caused from instantly varying rotating speed of an induction motor, the rotating speed is reconstructed based on the measured current signals. From several experiments, the proposed method shows a good estimation of the motor torque under the normal rotational speed.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.167-169
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• 2002

A sensorless drive has serveral advantages; low cost and mechanical simplicity. This paper studies a comparison between observer and Kalman filter for sensorless speed control of induction motor. The simulation result generally shows good performances in the both sensoless control strategies. In case of considering noises, EKF algorithm shows a better performance. In case of considering parameter variation, observer algorithm shows a better performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.794-803
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• 2016

This paper propose a sensorless control system of IPMSM with a adaptive back-EMF estimator and improved instantaneous reactive power compensator. A saliency-based back-EMF is estimated by using the adaptive algorithm. The estimated back-EMF is inputted to the phase locked loop(PLL) and the improved instantaneous reactive power(IRP) compensator for estimating the position/speed of the rotor and compensating the error components between the estimated and the actual position, respectively. The stability of the proposed system is achieved through Popov's hyper stability criteria. The validity of proposed algorithm is verified by the simulations and experiments.