• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.131-136
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• 2018

Reliable velocity estimation technology for trains is one of technologies used to operate trains safely and effectively. Various sensors such as tachometers, doppler radars, and global positioning systems are used to estimate velocity of a train. Tachometer is widely used to estimate velocity of a trains due to its simplicity, small volume, cost-effectiveness, continuously measurement at high speed, and robustness against noise. Accuracy in the velocity calculation using a tachometer depends on quantization error, measurement error of wheel radius or diameter, and tachometer's imperfection from manufacturing or installation process. In this paper, we present an accurate velocity estimation method using a low-resolution tachometer, which is commonly installed on a high-speed train. Baseline estimation method is proposed to accurately calculate the velocity of the high-speed train from tachometer's pulses. HEMU-430x test train is used for the experiment and verification of the proposed method. Experimental results with several routes show that the proposed method is more accurate than a conventional method.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.612-619
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• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.231-237
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• 2018

This paper investigates the impact of the channel variations and channel estimation errors on the error performance of convolutional coded STBC systems. We consider the orthogonal Almouti STBC and the quasi-orthogonal Jafarkhani STBC, and the error performance of the convolutional coded STBC system is investigated according to the channel variation and channel estimation error via numerical simulations. Simulation results show that, if the channel variation speed is slow, time diversity effects improve the error performance compared to the static-channel cases. However, if the channel variation speed is fast, unlike ZF or MMSE detection, the conventional STBC detection has the significant performance degradation especially with the quasi-orthogonal Jafarkhani STBC. Further, the error performance of the system is significantly degraded as the channel estimation errors become stronger, regardless of the detection scheme and channel variation speed.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.51-58
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• 2002

Vehicle dynamics control systems are. complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed is good results in normal conditions. But the estimation error in severe braking is discontented. In this paper, we estimate the absolute vehicle speed by using the wheel speed data from standard 50-tooth anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used. Ten algorithms are verified experimentally to estimate the absolute vehicle speed and one of those is perfectly shown to estimate the vehicle speed with a 4% error during a braking maneuver.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.192-195
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• 2002

The thesis proposes the sensorless vector control method that estimates the rotor speed and rotor time constant at the same time using stator current. In the proposed method, stator current error in the stationary reference frame is proportional to estimated speed error, and stator current error in the synchronous reference frame is proportional to estimated rotor time constant error. The proposed method can simultaneously produce a fast speed estimation and rotor time constant estimation. Therefore, this new method offers an improvement in the performance of a sensorless vector controller. And, the superiority of the proposed method is verified by simulation.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.505-508
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• 1999

Conventional adaptive speed estimators cannot avoid the influence of the non-linear inductance variation under the saturation conditions. Without speed sensors, it is difficult to identify the inductance variation using a reactive power mode because the model contains a term of the rotor speed. In this paper, we propose a novel speed estimator having hybrid architecture in order to estimate both the rotor speed and the inductance variation simultaneously when the motor flux is saturated. Proposed estimator consists of the error between the flux obtained from the stator voltage equation and the flux estimated from the rotor flux observer. Introducing a new correction term into the estimator increases the estimation ability of the conventional speed estimator even though the motor flux is saturated. The convergence of the speed estimation error is examined by simulation Furthermore, the experimental results show the validity of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.52 no.11
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• pp.577-585
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• 2003

This paper proposes a new speed sensorless control method of a permanent magnet synchronous motor using an instantaneous reactive power. In the proposed algorithm, the line currents are estimated by a observer and the estimated speed can be yielded from the voltage equation because the information of speed is included in back emf. But the speed estimation error between the estimated and the real speeds is occured by errors due to measuring the motor parameters and sensing the line current and the input voltage. To minimize the speed estimation error, the estimated speed is compensated by using an instantaneous reactive power. In this paper, the proposed algorithm is not affected by mechanical motor parameters because the mechanical equation is not used. The effectiveness of algorithm is confirmed by the experiments.

• 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.

• Journal of Power Electronics
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• v.17 no.1
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• pp.149-160
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• 2017

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.405-408
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• 2001

This paper investigates the problem of the speed estimation of conventional speed sensorless stator flux-oriented induction machine drive in the field weakening region and proposes a new speed estimation scheme to estimate speed exactly in transients in the field weakening region. The error included in the estimated rotor speed is removed by not a low pass filter but Kalman filter.