• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.243-248
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• 2010

This study addresses spacecraft moment of inertial estimation approach using Modified Rodrigues Parameters(MRP). The MRP offer advantage by avoiding singularity in Kalman Filter design for attitude determination caused by the norm constraint of quaternion parameters. Meanwhile, MRP may suffer singularity for large angular displacement, so that we designed appropriate reference attitude motion for accurate estimation. The proposed approach is expected to provide stable error covariance update with accurate spacecraft mass property estimation results.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.659-661
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• 1999

In this paper, we proposed a simple neural network-based parameter tuning algorithm, which could find the gradients of a certain performance index in the PID parameter spaces. In this process, we had to know the dynamics between input and output of the plant, and we used the Back Propagation Neural network to identify them. To make the parameter updating fast and smooth, we constructed the performance index as the sum of past N-squared plant errors, and applied a batch mode algorithm to update parameters. We performed several experiments with a DC Motor to show the validity of the proposed algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.3
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• pp.238-245
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• 2012

This paper presents a sensorless speed control of a 2-phase switch reluctance motor(SRM). The proposed sensorless control scheme is based on the slide mode observer with parameter compensator to improve the estimation performance. In the stand still position, the initial rotor position is determined by pulse current responses of each phase windings and the current difference. In order to determine an accurate initial rotor position, the two initial rotor positions are estimated by the difference of the pulse currents. From the stand still to the operating region, a simple open loop control which determines the commutation sequence by the pulse current of the unexcited phase winding is used. When the motor speed is reached to the sensorless control region, the estimated rotor position and speed by the slide mode observer are used to control the SRM. The flux calculator used in the slide mode observer is designed by phase voltage and the voltage drops in the phase resistance of the winding. The accuracy of the flux calculator is dependent on the phase resistance. For the continuous update of the phase resistance, current gradient at the inductance break point is used in this paper. The error of the estimated rotor position at the current gradient position is used to update the phase resistance to improve the sensorless scheme. The proposed sensorless speed control scheme is verified with a practical compressor used in home appliances. And the results show the effectiveness of the proposed control scheme.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.452-459
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• 2019

In this paper, we describe a PCM encoder structure that can update the telemetry system parameters in real time. In the PCM encoder, an analog signal control unit for FPGA, flash memory, and sensor data acquisition was constructed. UART communication, analog signal control, flash memory control, and frame generation are possible through logic inside FPGA of PCM encoder. UART communication allows the PC to transmit parameter data to the PCM encoder, and flash memory is controlled to update the parameter of the telemetry system in real time and finally the frame is formed. Simulation and verification were performed to confirm whether the parameter data is updated in real time, and the proposed structure was used to construct a telemetry system with enhanced flexibility and convenience.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.195-204
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• 2000

The finite element analysis (FEA) is widely used in modern structural dynamics because the performance of structure can be predicted in early stage. However, due to the difficulty in determination of various uncertain parameters, it is not easy to obtain a reliable finite element model. To overcome these difficulties, a updating program of FE model is developed by consisting of pretest, correlation and update. In correlation, it calculates modal assurance criteria, cross orthogonality, mixed orthogonality and coordinate modal assurance criteria. For the model updating, the continuum sensitivity analysis and design optimization tool(DOT) are used. The SENSUP program is developed for model updating giving physical parameter sensitivity. The developed program is applied to practical examples such as the BLDC spindle motor of HDD, and upper housing of induction motor. And the sensor placement for the square plate is compared using several methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.583-597
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• 2001

Tracking the targets of interest has been one of the major research areas in radar surveillance system. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than PDAF and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.2
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• pp.1-7
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• 2000

In this paper, an adaptive control law for nonlinear systems represented by input-output models are proposed under the assumption that unknown system parameters are in a known compact and convex set. Contrary to the previous results, the compact and convex set is not restricted to a ball whose center is at the origin or convex hypercube. It is proven that the proposed parameter update rule produces a sequence of parameters which reside in the set and guarantees that the position, velocity, and acceleration error converges to zero as time goes to infinity. This theoretical result was justified through simulations.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1730-1731
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• 2007

In this paper, we present a direct adaptive control method using neural networks for the control of nonlinear systems. The weights of neural networks are trained by an adaptive backpropagation algorithm based on Lyapunov stability theory. We develop the parameter update-laws using the neural network input and the error between the desired output and the output of nonlinear plant to update the weights of a neural network in the sense that Lyapunove stability theory. Beside the output tracking error is asymptotically converged to zero.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.689-692
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• 2003

In this paper, we propose an adaptive beamforming algorithm for array antenna. The proposed beamforming algorithm, based on Block LMS (Block - Least Mean Squares) algorithm, has a variable step size from coefficient update. This method shows some advantages that the convergence speed is fast and the calculation time can reduced using a block LMS algorithm from frequency domain. As the adaptive parameter approaches a stationary state, it could reduce the number of filter coefficient update with the help of various step size. In this paper we compared the efficiency of the proposed algorithm with a standard LMS algorithm which is a representative method of adaptive beamforming.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1129-1134
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• 2016

This paper suggests a new method of finding regularization parameter for image restoration problems. If the prior information is not available, separate optimization functions for Tikhonov regularization parameter are suggested in the literature such as generalized cross validation and L-curve criterion. In this paper, unified Bayesian interpretation of Tikhonov regularization is introduced and applied to the image restoration problems. The relationship between Tikhonov regularization parameter and Bayesian hyper-parameters is established. Update formular for the regularization parameter using both maximum a posteriori(: MAP) and evidence frameworks is suggested. Experimental results show the effectiveness of the proposed method.