• 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 KIEE Conference
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• 1997.11a
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• pp.509-511
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• 1997

A sensorless controller of induction motor has several advantage availability in a harsh environment. In this paper, the speed information is driving from the currents and the estimated stator flux. To obtain the estimated stator flux, this study is using the Estimator. The simulation results show that the proposed scheme has activity over a wide speed range and good response to load variations.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2149-2151
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• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.359-365
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• 2005

The absolute longitudinal speed of a vehicle is estimated by using data from an accelerometer of the vehicle and wheel speed sensors of a standard 50-tooth antilock braking system. An intuitive solution to this problem is, 'When wheel slip is low, calculate the vehicle velocity from the wheel speeds; when wheel slip is high, calculate the vehicle speed by integrating signal of the accelerometer.' The speed estimator weighted with fuzzy logic is introduced to implement the above concept, which is formulated as an estimation method. And the method is improved through experiments by how to calculate speed from acceleration signal and slip ratios. It is verified experimentally to usefulness of estimation speed of a vehicle. And the experimental result shows that the estimated vehicle longitudinal speed has only a $6\%$ worst-case error during a hard braking maneuver lasting a few seconds.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.8
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• pp.426-430
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• 1999

In this paper, a speed sensorless algorithm for a high-speed induction motor is proposed. The proposed algorithm simply estimates rotor speed by integrating the deviation between the command current value of a controller and the real current value of the motor. To estimate rotor speed without a speed sensor, a fuzzy speed controller and a neural network speed estimator are applied. Computer simulation and implementation of the proposed system is described.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.8
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• pp.899-905
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• 2014

In this paper, we propose a method that can be used to back-stepping controller design for speed control of Brushless Direct Current (BLDC) motor. First, back-stepping controller is designed with load torque estimator. The estimator is included to adapt to the variation of load torque in real time. Finally, the proposed controller is tested through experiment with a 120W BLDC motor for the angular velocity reference tracking performance and load torque volatility estimation. The simulation result verifies the performance of the proposed controller.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.867-872
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• 2022

The effect of load is an important factor in precise speed control of a motor. n this study, we design a state observer that can estimate and define one state of disturbance including errors and nonlinear terms of mathematical models, which is not easy with a mathematical model. Then, the observation gain is set so that the estimation error of the state observation converges to 0, and the estimated state is used in the back stepping controller to design a controller capable of precise speed tracking. As a result of applying to 1 [hw] class Interior Permanent Magnet Synchronous Motor, excellent stste variable observation and tracking performance can be confirmed.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1166-1171
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• 2001

The method for output disturbance estimation is proposed. In this method, output disturbance is estimated from the closed loop system dynamics using the output and control input signals. In the closed-loop output-disturbance estimator, precise system identification is required to reduce estimation error. The realization of estimator was done by the DSP board (DSPl103), and disturbance estimation in various environments was performed： change of rotation speed, media feature and spindle motor with (or without) auto-ball balancing system (ABS). From these experiments, the disturbance characteristics of ODD under various conditions are analyzed, and the desirable servo loop configuration based these results is proposed.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.36-46
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• 1994

A analog disturbance observer, which is suitable for servo motor drive, was employed to suppressi control of the impact drop disturbance. The main advantage of the observer is that a fast response disturbance estimation is obtained using the detected motor cuttent and speed. And the new M/T method for motor speed detection is proposed. The method should be able to reduce the detection dead time compared with the Ohmae's M/T method. Also, the low speed control of a servo motor using instantaneous speed detection method is described. To estimate the instantaneous speed from the average speed, the speed estimator of the first order is proposed. Actually we confirmed that these methods improve the speed control performance of the servo system with experiments.