• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.419-422
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• 2005

In this note, the Takagi-Sugeno (T-S) fuzzy-model-based state estimator using standard Kalman filter theory is investigated. In that case, the dynamic system model is represented the T-S fuzzy model with the fuzzy state estimation. The steady state solutions can be found for proposed modeling method and dynamic system for maneuvering targets can be approximated as locally linear system. And then, modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.245-248
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• 1998

This paper proposes a sensorless velocity estimator using the reduced-order state equation of induction motor based on Kalman Filter. The electrical transients in the stator voltage equations of induction motor are neglected in the reduced-order model. The advantage of using the reduced-order model is to reduce the required number of numerical integrations for filtering the rotor speed. As changing the operating points and the parameters of the induction motor in simulation studies, the behavior of the sensorless velocity estimator as predicted by the reduced-order state equation of induction machine is compared with the behavior predicted by the complete state equation of induction machine.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.866-869
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• 1988

In the operation of a power system, the security of the system has acquired significant importance to supply electric power of better quality. The State Estimator, a part of security functions, provides a complete real time solution estimate of the steady-state conditions of the power system for use by the Real Time Network Analysis functions. This paper briefly introduces the Fast Decoupled Weighted Least Square State Estimator which is adopted in the KEPCO EMS with features of Man-Machine Interface.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.2
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• pp.155-163
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• 1989

For a one link flexible arm control, quadratic optimal control is applied to the dynamic modilling which is derived from an assumed mode method. For the quadratic optimal control technique, the full state feedback must be obtained for closing the control loop, but because some of the states in the flexible system(e.g. the rate of change of the time dependent variables of the mode shapes) can not be directly measured, state estimator is necessary to achieve the practical implementation of the optimal controller. When disturbances and measurement noise occur, stochastic approach must be applied to estimating the states of the system. Kalman Filter is used as a stste estimator. Through the simulation, the flexible system with state estimator is compared with the flexible system assuming that all the states can be measured.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1865-1874
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• 2013

In this paper, a design method of speed and position estimator based on unscented Kalman filter is proposed for the no sensor control of IPMSM(Interior Permanent Magnet Synchronous Motor). The proposed method is simple more than the estimator designed with rotation axis for current measurement. Also the proposed state estimator is designed including nonlinear terms of the estimator. The controller which constructed using nonlinear back-stepping control method is operated speed and current control using the estimated speed and currents information. Through simulation, the performance of the designed estimator is compared to the estimator which is designed to synchronize d-q axis.

• 전기의세계
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• v.30 no.11
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• pp.728-733
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• 1981

Most of real world systems are highly non-linear. But due to difficulties in analyzing and dealing with it, only the linear system theory is well estabilished. Bilinear system where state and control are linear but not linear jointly is introduced. Here shows that optimal state estimation of stochastic bilinear system requirs infinite dimensional filter, thus onesub-optimal estimator for this system is suggested.

• 전기의세계
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• v.29 no.7
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• pp.471-477
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• 1980

This paper deals with a modified Kalman filter. An approaching horizon with a suitable initial condition will be considered, which is a little different from the classical Kalman filter. It will be shown in this paper that the new filter with approaching horizons is not only easy to computer but also possesses asymptotic stability properties. Thus this new estimatoris an excellent compromise between the ease of computation and the strict sense of optimality. When this estimator is used for the standard problem, the error covariance bound has been obtained. It is shown that the new estimator can be used as a suboptimal estimator which has a stability property. It is also demonstrated that the steady state Kalman filter can be obtained from the moving horizon estimator by taking the horizon parameter as infinity.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.180-189
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• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.