• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.544-548
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• 1999

Using Observer, on the sensorless vector control system is a novel techniques for modern induction motor control. In this paper, a speed estimation algorithm of an induction motor using an extended kalman filter was proposed. Extended kalman filter can solve the problem, that have steady state error of estimated speed in flux and slip estimation method. The extended Kalman filter is employed to identify the speed of an induction motor and rotor flux based on the measured quantities such as stator current and DC link voltage. In order to confirming above proposal, computer simulation carried out using Matlab Simulink and show the effectiveness of the control drives for induction motor speed estimation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.12
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• pp.971-982
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• 1989

An integrating position estimation algorithm has been developed for the navigation system of a free-ranging AGV system. The navigation system focused in this research work consists of redundant wheel encoders for the relative position measurement and a vision sensor for the absolute position measurement. A maximum likelihood method and an extended Kalman filter are implemented for enhancing the performance of the position estimator. The maximum likelihood estimator processes noisy, redundant wheel encoder measurements and yields efficient estimates for the AGV motion between each sampling interval. The extended Kalman filter fuses inharmonious positional data from the deadreckoner and the vision sensor and computes the optimal position estimate. The simulation results show that the proposed position estimator solves a generalized estimation problem for locating the vehicle accurately in space.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.2
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• pp.115-123
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• 2018

This paper discusses extended Kalman filter method for solving learning problems of multilayered neural networks. A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We consider an efficient learning algorithm for deep neural network. Extended Kalman filter method is applied to parameter estimation of neural network to improve convergence and computation complexity. We discuss how an efficient algorithm should be developed for neural network learning by using Extended Kalman filter.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.294-300
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• 2009

In high speed railway bridges, dynamic analysis is important because of high passing velocity and moving load at the regular intervals, and damping ratio is a major parameter to predict dynamic responses. In this paper, damping ratios were estimated by using half power band width method and extended Kalman filter according to acceleration signal conditions, and a relationship between estimated damping ratios and representative values of bridge vibration was derived. From the results, damping ratios estimated from total ambient vibration were more reliable than only free vibration part. In case of using extended Kalman filter, the estimated damping ratios varying with RMQ(root mean quad), as one of representative values of bridge vibration, have more feasible trend. Thus, it is shown that further studies on reliabilities of estimated damping ratios are needed.

• Proceedings of the KIEE Conference
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• summer
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• pp.1334-1336
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• 2004

The existing problems of the Electro-Magnetic Suspension system such as air-gap disturbance, mass variation and actuator/sensor failure are described in amore specific manner. These problem can not be solved by conventional state-feedback and output-feedback control. Extended Kalman Filter is to linearize about a trajectory that is continually updated with the state estimates resulting from the measurements. In this paper, first, the physical properties of the EMS system are described. second, Extended Kalman Filer designed as form appliable EMS system. It is shown that state estimation performance can be obtained with the use of Extended Kalman filter, and that results from simulation, stability analyze.

• Journal of the Korean Institute of Navigation
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• v.6 no.1
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• pp.61-72
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• 1982

As it is well-known, in recent years the Kalman filter has been extensively used in the engineering field. The authors tried to apply the extended Kalman filter for optimal estimation of ship's positiion which is fixed by simultaneous visual (or radio) bearings to two known locations. In practical piloting, bearings are generally measured intermittently, so in this case the original Kalman filter can not be applied because of the long sampling time. In this paper, the extended Kalman filter is revised in order to be used in such an unfavorable case, adn the Digital simulation is conducted by using the revised extended Kalman filter under various noise conditiions. Good results have been obtaiend and effectiveness of the proposed filter has been confirmed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.215-216
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• 2012

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.499-506
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• 2004

The Unscented Kalman Filter is known to outperform the Extended Kalman Filter for the nonlinear state estimation with a significance advantage that it does not require the computation of Jacobian but EKF has a competitive advantage to the UKF on the performance time. We compare both algorithms on training the artificial neural network. The validation data set is used to estimate parameters which are supposed to result in better fitting for the test data set. Experimental results are presented which indicate the performance of both algorithms.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.9
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• pp.636-645
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• 1988

A new filter algorithm is suggested improving structurally the conventional extended Kalman filter of which the performance is dependent on the selection of the reference axes, by use of line-of-sight axes and gain rotation technique. The implementation method using microcomputer which implements tracking Kalman filter is introduced in terms of hardware and software. Then, through the simulation the performance of suggested filter is compared with that of conventional extended Kalman filter and the possibility of the real time tracking of moving target is investigated.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.3
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• pp.217-222
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• 2016

This study investigates the design, analysis, and implementation of the square-root extended Kalman filter by using an algorithm derived by combining the Potter or Carlson algorithm with the modified Gram-Schmidt algorithm, for sensorless speed control of a permanent-magnet synchronous motor. The sensitivity of the Kalman filter to round-off errors is a well-known problem. A possible way to address this limitation is by combining the square-root concept and Kalman filter that can improve the numerical performance and solve instability-related problems such as divergence. This paper presents the design and analysis of the implementation of such a square-root extended Kalman filter. To demonstrate the performance of the proposed filter, experimental results under several operating conditions, such as high and low speeds, reversal rotation, detuned parameters and load test, have been analyzed. Further, code sizes and operation times have been compared. Experimental results establish the performance of the proposed square-root extended Kalman filter-based estimation technique for sensorless speed control of a permanent-magnet synchronous motor.