• 한국조명전기설비학회지:조명전기설비
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• 제9권6호
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• pp.41-48
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• 1995

본 연구에서는 Extended Kalman Filter(EKF)를 이용한 속도센서없는 우도전동기의 벡터제어의 구현을 제안하였다. 또한 회전자 저항의 변동을 보상 할 수 있도록 회전자 저항도 추정한다. 이산화된 유도전동기의 모델을 통해 유도 전동기의 속도와 회전자 저항을 포함한 상태 변수를 정의하고 벡터 제어에 필요한 자속각을 추정하여 노이즈 환경에 놓인 시스템의 동작 특성을 안정되게 하였다. EKF알고리즘의 연산을 위하여 DSP를 이용하고, 전류제어 장치로 공간 전압벡터 변조 방식의 적용이 용이한 마이크로 콘트롤러를 체용하고, 인버터는 IPM(Intelligent Power Module)으로 실험 장치를 구성하였다. 시뮬레이션과 실험을 통하여 속도 추정 특성과 회전자 저항 특성을 살펴본 결과, 본 논문의 EFK 알고리즘이 속도 센서없는 유도전동기 벡터제어에 적합함을 입증할 수 있었다.

• Structural Monitoring and Maintenance
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• 제3권2호
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• pp.115-127
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• 2016

It is a challenging problem of assessing the location and extent of structural damages with vibration measurements. In this paper, an improved Extended Kalman filter (EKF) with Tikhonov regularization is proposed to identify structural damages. The state vector of EKF consists of the initial values of modal coordinates and damage parameters of structural elements, therefore the recursive formulas of EKF are simplified and modal truncation technique can be used to reduce the dimension of the state vector. Then Tikhonov regularization is introduced into EKF to restrain the effect of the measurement noise for improving the solution of ill-posed inverse problems. Numerical simulations of a seven-story shear-beam structure and a simply-supported beam show that the proposed method has good robustness and can identify the single or multiple damages accurately with the unknown initial structural state.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.311-314
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.188-188
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• 2000

The most important problem in target tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters baged on the dynamic equations. In this paper, we propose the extended robust Kalman filter (ERKF) which can be applied to the real target tracking system with the parameter uncertainties. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter (EKF) via 3-dimensional target tracking example.

• The Journal of the Acoustical Society of Korea
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• 제20권1E호
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• pp.25-30
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• 2001

The unstable signature that is defined as frequency change with respect to the time or frequency modulation, is caused by the external loading variation in specific machinery component and Doppler shift etc. In this study, we analyze the generation mechanism of the unstable signature and apply the Extended Kalman filter (EKF) algorithm for its detection. The performance of Extended Kalman Filter is examined for numerical and measured signals and the results show its validity for unstable signature detection.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2011년도 한국우주과학회보 제20권1호
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• pp.25.4-25.4
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• 2011

Various estimation methods based on Kalman filter have been applied to the real-time satellite orbit determination. The most popular method is the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The EKF is easy to implement and to use on orbit determination problem. However, the linearization process of the EKF can cause unstable solutions if the problem has the inaccurate reference orbit, sparse or insufficient observations. In this case, the UKF can be a good alternative because it does not contain linearization process. However, because both methods are based on Gaussian assumption, performance of estimation can become worse when the distribution of state parameters and process/measurement noise are non-Gaussian. In nonlinear/non-Gaussian problems the particle filter which is based on sequential Monte Carlo methods can guarantee more exact estimation results. This study develops and tests the particle filter for satellite orbit determination. The particle filter can be more effective methods for satellite orbit determination in nonlinear/non-Gaussian environment.

• 전력전자학회논문지
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• 제13권5호
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• pp.336-343
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• 2008

본 논문에서는 병렬형 칼만 필터를 사용한 영구 자석 동기 전동기의 새로운 센서리스 제어 기법이 제안되었다. 제안된 기법은 기존의 확장형 칼만 필터(EKF)와는 달리 reduced-order EKF를 이용한 역기전력 추정 알고리즘을 통해 회전자 위치와 속도를 추정할 수 있고, 각각의 샘플링 시간마다 서로 다른 EKF를 실행하는 병렬형 구조를 사용함으로써 연산시간을 월등히 줄일 수 있다. 따라서 제안된 기법은 기존 EKF의 장점은 그대로 유지하며 단점으로 지적되었던 긴 연산시간 문제를 극복하고 쇄교 자속 값에 민감한 부분도 부분적으로 해결할 수 있다. 또한 운전 영역에 따라 그 형태를 달리함으로써 회전자 속도 및 위치를 안정적으로 추정할 수 있다. 제안된 기법은 실험 결과를 통하여 그 타당성이 검증되었고, 기존 EKF와의 연산 시간 비교를 통하여 우수성이 확인되었다.

• 전력전자학회논문지
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• 제16권3호
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• pp.266-273
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• 2011

본 논문에서는 매입형 영구자석 동기 전동기(IPMSM)의 확장형 역기전력(EEMF) 모델을 이용한 저감차수 병렬형 확장형 칼만 필터(EKF)를 이용한 센서리스 제어 기법을 제안한다. 제안된 센서리스 제어 기법은 간단한 수학적 구조로 매입형 영구자석 동기전동기 구동에 적합한 확장형 역기전력 모델을 이용하여 두 개의 저감 차수 형태로 표현하였다. 이러한 두 모델은 매 샘플링 시간마다 확장형 칼만 필터에 번갈아 연산된다. 행렬의 차수를 저감하여 EKF의 연산시간의 단축과 알고리즘 구현의 부담을 줄였으며 센서리스 제어의 안정적인 상태 벡터의 추정을 위해 병렬로 구동하는 두 모델에 의해 추정된 정보를 이용하였다. 제안된 기법은 실험 결과를 통하여 안정적인 위치 추정 및 속도 추정 성능을 검증 하였으며, 전 차수 EKF와의 연산 시간 비교를 통하여 우수성을 검증하였다.

• 전기학회논문지P
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• 제57권2호
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• pp.115-120
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• 2008

The EKF(Extended Kalman filter) method which is the state estimation algorithm of nonlinear stochastic system depends on the initial error and the estimated states. Therefore, the divergence of the estimated state can be caused if the initial values of the estimated states are not chosen as approximate real state values. In this paper, the demerit of the existing EKF method is improved using the EKF algorithm transformated by STWS(Single Term Walsh Series). This method linearizes each sampling interval of continous-time system through the derivation of an algebraic iterative equation without discretizing continuous system by the characteristic of STWS, the convergence of the estimated states can be improved. The validity of the proposed method is checked through comparison with the existing EKF method in simulation.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1520-1529
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• 2013

In this paper, we propose and assess the performance of "H infinity filter ($H_{\infty}$, HIF)" and "cost reference particle filter (CRPF)" in the problem of tracking a target based on the measurements of the range and the bearing of the target. HIF and CRPF have the common advantageous feature that we do not need to know the noise statistics of the problem in their applications. The performance of the extended Kalman filter (EKF) is also compared with that of the proposed filters, but the noise information is perfectly known for the applications of the EKF. Simulation results show that CRPF outperforms HIF, and is more robust because the tracking of HIF diverges sometimes, particularly when the target track is highly nonlinear. Interestingly, when the tracking of HIF diverges, the tracking of the EKF also tends to deviate significantly from the true track for the same target track. Therefore, CRPF is very effective and appropriate approach to the problems of highly nonlinear model, especially when the noise statistics are unknown. Nonetheless, HIF also can be applied to the problem of timevarying state estimation as the EKF, particularly for the case when the noise statistcs are unknown. This paper provides a good example of how to apply CRPF and HIF to the estimation of dynamically varying and nonlinearly modeled states with unknown noise statistics.