• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.270-276
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• 2002

An integrated robust fault diagnosis and fault accommodation strategy for a class of linear stochastic systems subjected to unknown disturbances is presented under the assumption that only a single fault may occur at a given time. The strategy is based on the fault isolation and estimation using a bank of robust two-stage Kalman filters and introduction of the additive compensation input for cancelling out the fault's effect on the system. Each filter is set up such that the residual is decoupled from unknown disturbances and fault with the influence vector designed in the filter. Simulation results for the simplified longitudinal flight control system with parameter uncertainties, process and sensor noises demonstrate the effectiveness of the present approach.

• Journal of Advanced Navigation Technology
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• v.2 no.1
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• pp.34-42
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• 1998

An adaptive state and input estimator for the tracking of a target with unknown randomly switching input is developed. In modeling the unknown inputs, it is assumed that the input sequence is governed by semi-Markov process. By incorporating the semi-Markov probability concepts into the Bayesian estimation theory, an effective adaptive state and input estimator which consists of parallel Kalman-type filters is obtained. Computer simulation results reveal that the proposed adaptive estimator have improved tracking performance in spite of the unknown randomly switching input.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.6
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• pp.530-537
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• 2014

A controller design method for a battery-energy storage system using a linearized battery model is presented in this paper. The suggested linear battery model is expressed with open-circuit voltage having three relaxation filters and a linear output equation. A method to obtain on-line resistance and maximum available power is also presented. The battery state of charge information is obtained by Kalman filter, and its performance is verified by FTP75 driving cycles. The controller for power converter is designed and experimented with a 250 V battery pack. The proposed control method is simple and easy to apply to a real system.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.137-145
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• 2005

A new design of contact detection algorithm is proposed for the z-axis of a wire bonder that interconnects between pads and leads in semiconductor manufacturing processes. Fast and stable contact detection of the z-axis is extremely important fer maintaining proper quality in the fine pitch gold wire bonding process, which has a small pad size of below 70um. The new method is based on a statistical approach and designed for the discrete Kalman filter. Real wire bonding experimental results are presented to demonstrate the advantages of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.221-227
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• 2002

In this paper, the system model for the compensation of the low-cost personal navigation system is derived and the error compensation method using GPS is also proposed. WNS(Walking Navigation System) is a kind of personal navigation system using the number of a walk, stride and azimuth. Because the accuracy of these variables determines the navigation performance, computational methods have been investigated. The step is detected using the walking patterns, stride is determined by neural network and azimuth is calculated with gyro output. The neural network filters off unnecessary motions. However, the error compensation method is needed, because the error of navigation information increases with time. In this paper, the accumulated error due to the step detection error, stride error and gyro bias is compensated by the integrating with GPS. Loosely coupled Kalman filter is used for the integration of WNS and GPS. It is shown by simulation that the error is bounded even though GPS signal is blocked.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.436-439
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• 1998

In this paper, we propose a centralized $H^{\infty}$ state estimator for the multi state estimation problem using the result suboptimal $H^{\infty}$ filter is a special form of Ka filter whose state equations are defined in md inner product spaces. Con- ventional decentr filters are based on Kalman filter assumes precesses and measurements noises are w Gaussian noise. Therefore, Kalman based decent filter design hasn't robust performance in situation. Simulation results show that decent $H^{\infty}$ filter has robust perfotmance in worst case sensor fault situation.

• Proceedings of the KIEE Conference
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• 2004.10a
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• pp.224-226
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• 2004

Digital Kalman filter is presented as a powerful approach to obtain the reference estimation of the control current for shunt active power filter. This algorithm provides the best estimate of the fundamental and harmonic frequency components from the sampled values of the line current or voltage. By adopting of the digital Kalman filtering algorithm, the structure of the control algorithm eliminates the need of a Phase locked loop(PLL) for the synchronization of the reference signal used in the compensation and it not sensitive to the distortion of the line voltage. The effectiveness of the algorithm is confirmed by the computer simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.517-523
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• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.

• Earthquakes and Structures
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• v.5 no.3
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• pp.359-378
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• 2013

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2167-2169
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• 2003

The LQG regulator presented in this dissertation is designed to improve system stability with the structure in which Kalman Filters and LQ regulator are inter-connected to avoid disturbances on the system. For the sake of performance evaluation of the LQG regulator in this dissertation, the regulator is put in use for the speed control of DC Servo Motors without speed sensors on them to realize speed control without sensors by estimating uncertain speed with its state signals and to construct an optimal controller using the LQG regulator. Furthermore, state estimation process with white noise entered onto the systems is been experimented through a series of simulation. As a consequence, the process of estimating speed signals out of the signals with white noise inputs is confirmed using Kalman Filter.