• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.983-984
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• 2011

An effective estimation method of equivalent circuit parameters of three-phase induction motor by using FEM is presented in this paper. Arbitrary three torque-speed points at asynchronous speed on the torque-speed curve are required by using time-stepping FEM, then the equivalent circuit parameters of IM can be calculated rapidly with presented method. After obtaining the equivalent circuit parameters, the general analysis method based on equivalent circuit can be used to get entire torque-speed curve very quickly. Although the purely numerical method such as FEM also can estimate torque-speed curve directly and accurately, however, usually this process is time consuming.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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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.

• Proceedings of the KIEE Conference
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• 2006.04a
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.750-755
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• 2013

In-wheel motor system gets the driving force from direct-driven motor in the wheel of electric vehicle. It is known as good system for vehicles, from an efficiency, packaging, handling and safety. This paper describes motor and inverter technologies, system configuration and control algorithms for in-wheel type electric vehicle. It is necessary to control on an interrelation perspective because this system drives two motors at same time. In system design, IPMSM(Interior Permanent Magnet Synchronous Motor) including a wide operating range and high-speed rpm is used and flux weakening control is performed in constant power range. Under the torque command from the host controller, auto control box, inverter's output torque is calculated with using torque estimation technique and applied to actual vehicle driving system. It is verified that the configuration and the algorithm are suitable for the in-wheel motor system.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.3
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• pp.300-305
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• 2013

The torque was calculated with inductance and iron loss. Because the linkage flux can change the inductance, and q-axis current can change the iron loss. Therefore, precise estimation of torque can achieve with the inductance and iron loss detail calculations. So, in this paper, the d, q-axis inductance was verified through CVCT(Current Vector Control Test) and DCT(Direct Current Test). Also in the iron loss calculation, the prediction of all areas of current magnitude, phase angle and speed was very difficult. And LUT(Look-Up Table) was spent time and resource largely. Therefore, iron loss mathematization was proposed according to current magnitude, phase angle and speed. Also, characteristics of IPMSM were comprised of analyzed and experimental values.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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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.

• Journal of Power Electronics
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• v.10 no.2
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• pp.176-180
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• 2010

This paper deals with a control method to reduce the torque ripple of a permanent magnet synchronous motor (PMSM) for electric power steering (EPS) systems. Such an application requires a very low torque ripple in order to maintain a good steering feel. However, because of spatial limitations, it cannot help having a partial saturation in the iron core of the PMSM for an EPS system, and this saturation results in a significant torque ripple. Thus, this paper analyzes the torque ripple caused by the magnetic saturation of a PMSM and proposes a method with respect to inductance measurement to verify the partial saturation. In addition, it is shown that a compensation current is needed in order to minimize the torque ripple when a PMSM is driven in the high torque region. The estimation process of the current and the torque ripple decreased by the current are presented and verified with test results.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.31-46
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• 2009

An Unscented Kalman Filter (UKF) for estimation of the attitude and rate of a spacecraft using only magnetometer vector measurement is developed. The attitude dynamics used in the estimation is the nonlinear Euler's rotational equation which is augmented with the quaternion kinematics to construct a process model. The filter is designed for small satellite in low Earth orbit, so the disturbance torques include gravity-gradient torque, magnetic disturbance torque, and aerodynamic drag torque. The magnetometer measurements are simulated based on time-varying position of the spacecraft. The filter has been tested not only in the standby mode but also in the detumbling mode. Two types of actuators have been modeled and applied in the simulation. The PD controller is used for the two types of actuators (reaction wheels and thrusters) to detumble the spacecraft. The estimation error converged to within 5 deg for attitude and 0.1 deg/s for rate respectively when the two types of actuators were used. A joint state parameter estimation has been tested and the effect of the process noise covariance on the parameter estimation has been indicated. Also, Monte-Carlo simulations have been performed to test the capability of the filter to converge with the initial conditions sampled from a uniform distribution. Finally, the UKF performance has been compared to that of the EKF and it demonstrates that UKF slightly outperforms EKF. The developed algorithm can be applied to any type of small satellites that are actuated by magnetic torquers, reaction wheels or thrusters with a capability of magnetometer vector measurements for attitude and rate estimation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1047-1052
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• 2017

In this paper, the calculation torque using the d-q axis has advantage like faster execution time. However, the torque ripple can't be considered in the torque calculation using d-q axis equivalent circuit because the time-dependent component is removed. When d-q transformation was performed, it was founded that some parameters has some characteristics. These characteristics were considered for representing torque ripple. The calculation with d-q axis transformation and Finite Element Analysis(FEA) were performed, and the results were compared. As a result, it was validated that the calculated torque can be expressed with ripple.

• Journal of Power Electronics
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• v.10 no.5
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• pp.498-504
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• 2010

The output power fluctuations of renewable energy power plants such as wind turbine generators and photovoltaic systems result in frequency deviations and terminal voltage fluctuations. Furthermore, these power fluctuations also affect the turbine shaftings of diesel generators and gas-turbine generators which are the main power generation systems on isolated islands. Therefore, it is important to achieve torsional torque suppression. Since the measurement of torsional torque is technically difficult, and there is an uncertainty in the mechanical constants of the shaft torsional system. This paper presents an estimation system that estimates torsional torque by using a developed $H_{\infty}$ observer. In addition to the above functions, the proposed shaft torque observer incorporates a parameter identification system that aims to improve the estimation accuracy. The simulation results validate the effectiveness of the proposed $H_{\infty}$ observer and the parameter identification.