• 전력전자학회논문지
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• 제24권1호
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• pp.33-39
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• 2019

We propose a mechanical parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using a sliding-mode observer (SMO) and an adaptive filter. The SMO estimates system disturbances in real time, which contain the information on mechanical parameters. A desirable feature that distinguishes the proposed estimation algorithm from other existing mechanical parameter estimators is that the adaptive filter estimates electromagnetic torque to improve the estimation performance. Moreover, the SMO acts as a low-pass filter to suppress the chattering effect, which enables the smooth output signals of the SMO. We verify the mechanical parameter estimation performance for SPMSM by conducting extensive experiments for the proposed algorithm.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권5호
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 전기학회논문지
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• 제60권2호
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• pp.293-300
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• 2011

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.232-236
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• 2000

In this paper a novel estimation algorithm of mechanical parameters in two-mass system is proposed. The inertia of a load and a motor and the stiffness are estimated by using RLS (Recursive Least Square) algorithm and acceleration information of motor. The effectiveness of the proposed scheme is verified with simulation and experiments results.

• 대한기계학회논문집A
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• 제28권3호
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• pp.238-244
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• 2004

To monitor the torque of an induction motor using current, the accurate identification of the motor parameters is very important. In this study, the motor parameters such as rotor resistance, stator and rotor leakage inductance, mutual inductance are estimated for torque monitoring and indirect vector control. Estimated parameters are used to monitor the torque of vector controlled induction motor without any speed measuring sensor. Stator current is measured to estimate the magnetizing current which is used to calculate flux linkage, rotor velocity and motor torque. From the experiments, the proposed method shows a good estimation of the motor parameters and torque under the normal rotational speed.

• Journal of Power Electronics
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• 제3권2호
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• pp.99-114
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• 2003

This paper presents a feasible development on a highly accurate quick response adjustable speed drive implementation fur general purpose induction motor which operates on the basis of sensorless slip frequency type vector controlled sine-wave PWM inverter with an automatic tuning machine parameter estimation schemes. In the first place, the sensorless vector control theory on the three-phase voltage source-fed inverter induction motor drive system is developed in slip frequency based vector control principle. In particular, the essential procedure and considerations to measure and estimate the exact stator and rotor circuit parameters of general purpose induction motor are discussed under its operating conditions. The speed regulation characteristics of induction motor operated by the three-phase voltage-fed type current controlled PWM inverter using IGBT's is illustrated and evaluated fur machine parameter variations under the actual conditions of low frequency and high frequency operations for the load torque. In the second place, the variable speed induction motor drive system, employing sensorless vector control scheme which is based on three -phase high frequency carrier PWM inverter with automatic toning estimation schemes of the temperature -dependent and -independent machine circuit parameters, is practically implemented using DSP-based controller. Finally, the dynamic speed response performances for largely changed load torque disturbances as well as steady state speed vs. torque characteristics of this induction motor control implementation are illustrated and discussed from an experimental point of view.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1166-1176
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• 2017

Estimation errors of the rotor speed and position in sensorless control systems of Permanent Magnet Synchronous Motors (PMSM) will lead to low efficiency and dynamic-performance degradation. In this paper, a parallel-type extended nonlinear observer incorporating the nominal parameters is constructed in the stator-fixed reference frame, with rotor position, speed, and the load torque simultaneously estimated. The stability of the extended nonlinear observer is analyzed using the indirect Lyapunov's method, and observer gains are selected according to the transfer functions of the speed and position estimators. Taking into account the parameter inaccuracies issue, explicit estimation error equations are derived based on the error dynamics of the closed-loop sensorless control system. An equivalent flux error is defined to represent the back Electromotive Force (EMF) error caused by the inaccurate motor parameters, and a compensation strategy is designed to suppress the estimation errors. The effectiveness of the proposed method has been validated through simulation and experimental results.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제50권4호
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• pp.151-157
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• 2001

This paper presents an identification method of parameters of induction motor which is driven by PWM voltage inverter. The method uses least square estimation based on the step voltage input and current response. Utilizing the fact that ratio of two characteristic roots is large in the induction motor circuit, we derived two 1st-order difference equations for direct computation of parameter values. experimental results are compared with conventional motor test results to demonstrate that the proposed method is capable of estimating parameters of induction motor at standstill.