• Smart Structures and Systems
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• 제11권1호
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

• 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.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.142-148
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• 2008

In this paper, we deal with the problem of reducing the order of the detection filter for the linear time-invariant system. Even if the detection filter is generally designed in the form of full order linear observer, we show that it is possible to reduce its order when the response of fault signals is limited to a subspace of the estimation state space. We propose a method to extract the subspace using the observer canonical form considering the dynamics related to the remaining subspace acts as a disturbance. We designed a reduced order detection filter to reject the disturbance as well as to guarantee fault detection and isolation. A simulation result for a 5th order system is presented as an illustrative example of the proposed design method.

• 센서학회지
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• 제28권4호
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• pp.216-220
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• 2019

In this paper, we propose a finite memory filter (estimator) for discrete-time stochastic linear systems with delays in state and measurement. A novel filtering algorithm is designed based on finite memory strategies, to achieve high estimation accuracy and stability under parametric uncertainties. The new finite memory filter uses a set of recent observations with appropriately chosen initial horizon conditions. The key contribution is the derivation of Lyapunov-like equations for finite memory mean and covariance of system state with an arbitrary number of time delays. A numerical example demonstrates that the proposed algorithm is more robust and accurate than the Kalman filter against dynamic model uncertainties.

• 한국항공우주학회지
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• 제48권11호
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• pp.881-888
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• 2020

본 논문에서는 관측자가 얻을 수 있는 시선각(LOS) 측정값을 사용하여 관심표적의 상태변수를 추정하는 연구를 수행하였다. 관심상태변수는 표적의 위치, 속도 및 가속도로 설정하였다. 시선각 측정값은 필터에 표적운동모델 적용을 어렵게 하는 비선형성이 강한 측정값이다. 이러한 문제해결을 위해 가측정치 공식(Pseudomeasurement equation)을 사용하여 시선각 측정값 수식을 변경한 후 3D 가변선회(3D Variable Turn) 표적운동모델을 적용하였다. 또한 필터의 성능을 위해 기구학적구속조건(Kinematic Constraint)을 적용하였다. 필터는 초기조건에 강건한 특성을 가진 Bias Compensation Pseudomeasurement Filter (BCPMF)를 사용하였다. 병렬 계산의 이점을 위해 Two Stage Kalman Filter 형태를 추가적으로 적용하였다. 이 기법들을 사용하여 TBCPMF 3DVT-KC 필터를 제안하였고 시뮬레이션을 통해 성능을 확인하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제14권1호
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• pp.45-51
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• 2014

Thermal shock tests at two stress levels were performed to see the life (cycles) of LPF ASSY (low pass filter assembly) at normal stress level. In this case Coffin-Manson relationship is generally used to describe the relationship between the temperature difference and the life, together with the Weibull distribution describing the life at each stress level. So for given data Coffin-Manson is fitted to predict the life at normal stress level. However, different types of models are appropriate for this type of test. Hence, a more appropriate model such as General log-linear model which can also incorporate the duration at the highest and lowest temperatures and acceleration time will be introduced.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.864-866
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• 1996

The attitude error is expressed using four kinds of quaternion errors. And the explicit relation equations between them are derived four kinds of nonlinear error models of SDINS using the their explicit relation are also proposed for a nonlinear filter which may be available for a system in the presence of a large attitude error the concept of the proposed nonlinear error model is applied to the velocity aided SDINS using a linear Kalman filter and an extended Kalman filter the simulation results reveal a improvement of performance using the nonlinear error model.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1788-1789
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• 2011

In order to develop a one-axis gimbaled acoustic source tracker for mobile robots, a pseudo-linear direction of arrival(DOA) estimator is proposed using a linear ultrasonic sensor array. Under the assumption that the sensor measurement errors are negligible, a linear measurement model is derived using the linear prediction relation of the received sinusoidal acoustic signals. Applying the Kalman filtering technique for this model, the linear recursive DOA estimator is designed. For its linear recursive filter structure, it is preferable for real-time implementation on a commercial DSP. Through the experiments, the effectiveness of the suggested method is demonstrated.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.980-983
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• 1996

This paper presents the method of determining state feedback gains of XPTOS for disk drive servomechanism based BESSEL filter prototype. A typical disk drive actuator can be modeled as second order dynamics for low frequencies. However, the response at higher frequencies shows resonant behavior which cannot be easily modeled. XPTOS consists of the nonlinear control region and the linear control region. In the linear control region, the poles of a second order nominal model of plant must be properly relocated by pole placement technique to attenuate resonant modes at high frequency and to attain minimum time state transition. It is difficult to select position to satisfy this object because velocity feedback gain is subjected to position feedback gain in XPTOS. Here poles of BESSEL filter prototype are selected to determine state feedback gains of XPTOS. Simulation results for disk drive servomechanism using XPTOS having state feedback gains by the proposed method are presented.