• 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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• 2001.07a
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• pp.389-392
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• 2001

This paper presents neural load disturbance 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 proposed. 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 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.

• 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

• 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.9 no.1
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• pp.42-49
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• 2004

This paper presents a new method of external load disturbance compensation using deadbeat load torque observer and gain compensation by parameter estimator. The response of the permanent magnet synchronous motor(PMSM) follows the nominal plant. The load torque compensation method is composed of a deadbeat observer. To reduce the noise effect, the post-filter implemented by moving average(MA) process is adopted. The parameter compensator with recursive least square method(RLSM) parameter estimator is suggested to make the new system work as same as the name plate system which in used to take gains. The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system has a robust and precise system against the load torque and the parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) 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 load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.3
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• pp.430-438
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• 2019

For a pump or a compressor motor, a high periodic load torque variation is induced by the mechanical works, and it causes system vibration and noise. To minimize these problems, load torque compensation method, injecting periodic torque current, could be utilized. However, with the sensorless control method, which is usually utilized in the pump and compressor for low cost, the periodic torque current degrades the accuracy of the rotor position estimation owing to the inductance variation. This paper analyzes the rotor position and speed estimation error of sensorless control method with constant motor parameters under period loading. Assuming the constant speed by the accurate load torque compensation, the speed error equation is derived in frequency domain with inductance depending on the stator current. Further, it is also shown that the rotor position error could be minimized by compensating the inductance variation. The simulation and experimental results verify that the derived speed error model and the validity of the inductance compensation method.

• Journal of Power Electronics
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• v.18 no.3
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• pp.736-745
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• 2018

This paper presents an extended state observer (ESO) based direct torque control (DTC) for use in induction motor systems to handle the issues of time delays, load torque disturbances and parameter uncertainties. Direct torque control offers an excellent torque response and it does not require a proportion integration (PI) controller in the current loop. However, a PI controller is still adopted in the outer speed loop to generate the torque reference value, which is a slow method. An ESO based compound control scheme is proposed to improve the response rate and accuracy of the torque reference signal, especially when load torque is injected. In addition, the time delay problem is analyzed and compensated for in this paper to reduce torque ripples. The proposed disturbance compensation technique based direct control scheme is shown to have good performance both in the transient and stable states via simulations and experimental results.

• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an 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 load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.92-95
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• 2001

This study presents neural load disturbance observer that used to deadbeat load torque observer and regulation of the compensation gain by parameter estimator. As a result, the response of PMSM fellows 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 proposed. 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 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.