• Proceedings of the KIPE Conference
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• 2003.07a
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• pp.393-397
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• 2003

This paper presents neural load torque observer tha used to deadbeat load torque observer and regulation of the compensation gun by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator li combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper

• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.6
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• pp.587-592
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• 2013

In this paper, the performance improvement of the sensorless control of IPMSM employing the active flux concept by the improved current estimator is presented. The accuracy of the current estimator used in a previous report is degraded when the motor parameters are not known exactly. A simple current estimator derived from estimated flux is proposed to improve the position estimation performance. In order to show the usefulness of the proposed estimation method, the simulation results using Matlab/Simulink and the experiment results are presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.9
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• pp.1859-1865
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• 2002

In the conventional sliding mode control technique, a priori knowledge of the bound of external disturbances or/and parameter uncertainties is required to assure control robustness. This, however, may not be easy to obtain in practical situation. This work presents a novel methodology, a sliding mode controller with perturbation estimator, which offers a robust control performance without a priori knowledge about the perturbations (disturbances and parameter uncertainties). The proposed technique is featured by an integrated average value of the imposed perturbation over a certain sampling period. In order to demonstrate the effectiveness of the proposed methodology, a two-link robotic system is adopted and its position control performance is evaluated. In addition, a comparative work between the conventional technique and the proposed one is undertaken.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.285-288
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• 2002

This paper presents external load disturbance compensation that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a deadbeat observer To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM(recursive least square method) parameter estimator is suggested to increase the performance of the load torque observer and main controller The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• Proceedings of the KIPE Conference
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• 2002.11a
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• pp.49-52
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• 2002

This paper presents neural load torque observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM follows that of the nominal plant. The load torque compensation method is compose of a neural deadbeat observer. To reduce of the noise effect, the post-filter, which is implemented by MA process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural torque observer to resolve the problems. As a result, the proposed control system becomes a robust and precise system against the load torque and the parameter variation. A stability and usefulness, through the verified computer simulation, are shown in this paper.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.4
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• pp.298-301
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• 2021

A mechanical angle estimator is presented in this study to achieve the sensorless control of permanent magnet synchronous motor (PMSM) used in driving a reciprocating compressor. Braking the PMSM at a specific mechanical angular position is critical for the silent stoppage of the reciprocating compressor. The performance of conventional mechanical angle observers used in reciprocating compressor drives can be seriously affected according to gains of the speed controller because such observers rely on the magnitude of current ripples. A speed ripple-based mechanical angle estimator is proposed to solve this problem. Experimental results showed the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.4
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• pp.329-332
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• 2020

A back-electromotive force (back-EMF) estimator for a permanent magnet synchronous motor (PMSM) uses the three-phase voltage references of a current controller to estimate rotor position. However, owing to voltage drops caused by the nonlinear characteristics of switches and passive components, the actual voltage in the motor and the three-phase voltage reference may not match. This study proposes a sensorless control method using a sine-wave output filter applied between the motor drive system and PMSM. The precise voltage in the motor can be measured with the sine-wave output filter and applied to the input of the estimator. Moreover, given that the voltage in the motor can be measured precisely at extremely low speeds, the stable operation range of the back-EMF estimator can be secured. Experimental results show that the proposed sensorless control method has stable operation at extremely low speeds compared with conventional sensorless control.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.350-355
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• 1997

The problem of mathematical model for an unknown system by measureing its input-output data pairs is generally referred to as state estimates. The state estimation problem is often of importance in its own right since we may want to know the value of the states. For instance, in navigation, we may take noisy positional fixes using satelite or radar navigation, and the estimator can use these measurements to provide accurate estimates of current position, hedaing, and velocity. And the state estimates can also be used for control purposes. Then it is very important to know the state of plant. In this paper, the theory of the minimization of a loss function was used to design the fuzzy system. Here, the used teory is Least Square Esimation method. This parametrization has the Linear in the parameters charcteristic that allows standard parameter estimation technique to be used to estimate the parameters of the fuzzy system. The combination of the fuzzy system and the estimation m thod then performs as a nonlinear estimator. If several fuzzy label are defined for the input variables at the antecedent part, the fuzzy system then behaves as a collection of nonlinear estimators where different regions of rules have different parameters. In simulation results, the fuzzy model controlled a difference in the structure between the actual plant and the fuzzy estimator. It is also proved that the fuzzy system is equivalent to its transformed system. therefore we was able to get the state space equation of system with the estimated paramater.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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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 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.5
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• pp.40-47
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• 2012

The PMSM position sensor using two rectangular hall sensors can restrictively acquire the 90[$^{\circ}$] position information of rotor according to electrical angle. Thus, the control method using this position sensor cannot react properly to a rapid load torque change. On the other hand, even though a sensorless method has the advantage of acquiring instantaneous rotor position information, the accuracy of position sensor can be determined by the gain value of estimator. This paper suggests a robust speed control method on torque fluctuation condition, which combines low cost two rectangular hall sensors and sensorless control method.