• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.307-312
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• 2007

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of induction motor using FLC-FNN and estimation of speed using ANN controller The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.591-593
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• 1996

In order to implement the direct vector control type sensorless vector control, the rotor flux and the angular speed of the rotor can only be estimated through the measurement of the stationary voltage and current states. To estimate the rotor flux, the use of the rotor flux observer(RFO) has been proposed. It is known that the RFO is relatively insensitive to parameter variations. Using the rotor flux value obtained from the RFO, the rotor flux vector can be estimated. The angular speed of the rotor is estimated by the difference between the synchronous angular speed and the slip angular speed, both of which are derived from the rotor flux vector. However unwanted high order frequency waves become incorporated into the synchronous angular speed during calculations. Thus we propose the use of digital filters that will eliminate these high frequency waves. We have demonstrated through computer simulations that the use of filters results in stable system activity over a wide speed range and good response to load variations.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.240-243
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• 2003

Sensorless Vector control of the permanent magnet synchronous motor(PMSM) typically requires knowledge of the PMSM structure and parameters, which in some situations are not readily available or may be difficult to obtain. In this paper, by measuring the currents of the PMSM drive, a neural-network-based rotor position and speed estimation method for PMSM is described. Because the proposed estimator treats the estimated motor speed as the weights, it is possible to estimate motor speed to adapt back propagation algorithm with 2 layered neural network. The proposed control algorithm is applied to PMSM drive system. The operating characteristics controlled by neural networks control are examined in detail.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.4
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• pp.299-306
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• 2003

The rotor flux position is needed to perform vector control of induction motor. But rotor speed information is needed to get accurate the rotor flux position. It is difficult to implement the open loop method without speed information or the motor equation only because of noise or the motor parameter error. This paper presents the speed estimator can use the arbitrary rotor flux observer by separating the flux observer and speed estimator and apply the three flux observers proposed by Ohtani, Lorenz and full order flux observer. The validity of speed estimator presented is verified and the performance using the three rotor flux observers is analyzed by the simulation and experiment.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.9
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• pp.626-634
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• 2000

The paper describes a new and rigorous mathematical model using counter-EMF for the rotor field oriented system with induction motor which uses the estimated speed and rotor flux based on a Model Reference Adaptive System as well as the real-time approach. The estimated speed and rotor flux is used for the speed and flux feedback control. The stability and the convergence of the estimator are improved on the basis of hyperstability theory for non-linear systems. The validity of the proposed method is verified by simulation and also the sensorless control was tested on the propulsion system simulator used for the development of Korean High-Speed Railway Train(KHSRT).

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.1
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• pp.37-46
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• 2004

A novel rotor speed estimation method using model reference adaptive control(MRAC) is proposed to improve the performance of a sensorless vector controller. In the proposed method, the stator current is used as the model variable for estimating the speed. In conventional MRAC methods, the relation between the two model errors and the speed estimation error is unclear. In the proposed method, the stator current error is represented as a function of the first degree for the error value in the speed estimation. Therefore, the proposed method can produce a fast speed estimation. The robustness of the rotor flux-based MRAC, back EMF-based MRAC, and proposed MRAC is compared based on a sensitivity function about each error of stator resistance, rotor time constant, mutual inductance. Consequently, the proposed method is much more robust than the conventional methods as regards errors in the mutual inductance, stator resistance. Therefore, the proposed method offers a considerable improvement in the performance of a sensorless vector controller at a low speed. In addition, the superiority of the proposed method and the validity of sensitivity functions were verified by simulation and experiment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.7
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• pp.113-122
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• 2002

Control system modeling and optimal bending filter design for KSR-III (Korea Sounding Rocket III) are performed. Rigid rocket dynamics, aerodynamics, sloshing, structural bending, actuator dynamics, sensor dynamics and on-board computer characteristics are considered for control system modeling. Compensation for time-varying control system parameters is conducted by gain-scheduling. A filter to stabilize bending mode is designed using parameter optimization technique. Resultant attitude control system can satisfy required frequency domain stability margin.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4A
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• pp.399-406
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• 2011

Adaptive beamformers, which utilize a priori information on the arrival angle of the desired signal. suppress interferences while maximizing their gains in the desired signal direction. However, if there exist errors in the direction information, they can suffer from severe performance degradation since the desired signal is treated as an interference. A robust adaptive beamforming method is presented which exploits the signal-blocking structure of the Duvall beamformer. The proposed method finds an interference signal space directly from correlations of received signals and then obtains a weight vector such that it is orthogonal to the space. Applying the weight vector to two sub arrays which consist of one less sensors than the original uniform lineal array (ULA), the beamformer efficiently estimates the arrival angle of the desired signal. Its computational complexity is lower than existing methods, which require matrix inversion or eigendecomposition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.551-559
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• 2005

Estimation of unknown signal parameters with sensor array measurements has been investigated quite extensively. Also, there has been in recent years an explosive increase in the number of mobile users in wireless cellular systems, thus contributing to growing levels of multi-user interference. To overcome this problem, application of adaptive antenna array techniques to further increase the channel capacity has been discussed. In this paper, a new model of locally scattered signals in the vicinity of mobiles is proposed by defining the mean steering vector and is manipulated mathematically for several distributions. Under this model an estimation method of the angle of arrival(AOA) is investigated based on a weighted subspace fitting(WSF) technique. Statistical analysis and simulations are also considered.

• Journal of Advanced Marine Engineering and Technology
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• v.26 no.6
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• pp.695-704
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• 2002

Generally, induction motor controller requires rotor speed sensor for commutation and current control, but it increases cost and size of the motor. So in these days, various researches including speed sensorless vector control have been reported and some of them have been put to practical use. In this paper a new speed estimation method using neural networks is proposed. The optimal neural network structure was tracked down by trial and error, and it was found that the 8-16-1 neural network has given correct results for the instantaneous rotor speed. Supervised learning methods, through which the neural network is trained to learn the input/output pattern presented, are typically used. The back-propagation technique is used to adjust the neural network weights during training. The rotor speed is calculated by weights and eight inputs to the neural network. Also, the proposed method has advantages such as the independency on machine parameters, the insensitivity to the load condition, and the stability in the low speed operation.