• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1146-1151
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• 2005

This paper deals with the control problem of a natural circulation water tube boiler with constraint conditions. Some linearized models for the water tube boiler are proposed around some operating points, and the model based predictive control law is adopted to control the plant accounting for constraints. In this controller, the Kalman filter is used for the state estimation, and the controller is designed based on the linearized model. The control performance of the designed controller is exemplified via some nonlinear simulations around the operation point, which show it works well.

• 전기학회논문지
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• 제66권2호
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• pp.468-473
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• 2017

This paper presents an in-process measurement system for shaft radius measurement during grinding process. This system does not require to stop the grinding process, which can enhance productivity and quality. In order to measure the radius, the system employs an eddy current sensor that can measure without any contact with the shaft. This type of sensor is very appropriate because it is insensitive to interference such as cutting fluid, coolant, contact pressure, and wear. For data analysis, the measurement system is modeled as a linearized discrete form where the states with noise are estimated by an extended Kalman filter. This system has been validated through simulations and experiments.

• 전력전자학회논문지
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• 제19권6호
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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.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2017년도 제48회 춘계학술대회논문집
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• pp.218-223
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• 2017

본 논문은 75톤급 액체로켓엔진의 상태진단을 위해 칼만 필터를 이용한 고장진단 연구를 수행하였다. 칼만 필터 설계를 위해 75톤급 액체로켓엔진 비선형 시뮬레이션 모델을 공칭 작동점에서 선형화하였으며, 정상 모델의 상태량 변수 4가지를 이용하여 측정값과 추정값 비교를 통해 칼만 필터의 성능을 확인하였다. 이를 이용한 고장진단 알고리즘의 성능을 확인하기 위하여 터보펌프 고장을 모사하였으며 정상 모델의 잔차 변화를 비교하여 칼만 필터를 이용한 고장진단이 가능함을 확인하였다.

• ETRI Journal
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• 제34권3호
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• pp.379-387
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• 2012

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

• 한국정밀공학회지
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• 제16권3호통권96호
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• pp.136-144
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• 1999

This paper presents an in-process measurement system for shaft radius measurement during grinding process. This system does not require to stop the grinding process, which can enhance productivity and quality. In order to measure the radius, the system employs an eddy current sensor that can measure without any contact with the shaft. This type of sensor is very appropriate because it is insensitive to interference such as cutting fluid, coolant, contact pressure, and wear. For data analysis, the measurement system is modeled as a linearized discrete form where the states with noise are estimated by an extended Kalman filter. This system has been validated through simulations and experiments.

• 제어로봇시스템학회논문지
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• 제21권1호
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• pp.34-39
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• 2015

In this paper, a finite impulse response (FIR) filter is proposed for discrete-time nonlinear systems. The proposed filter is designed by combining the estimate of the perturbation state and nominal state. The perturbation state is estimated by adapting the optimal time-varying FIR filter for the linearized perturbation model and the nominal state is directly obtained from the nonlinear nominal trajectory model. Since the FIR structured estimators use the finite horizon information on the most recent time interval, the proposed extended FIR filter satisfies the bounded input/bounded output (BIBO) stability, which can't be obtained from infinite impulse response (IIR) estimators. Thus, it can be expected that the proposed extended FIR filter is more robust than IIR structured estimators such as an extended Kalman filter for the round-of errors and the uncertainties from unknown initial states and uncertain system model parameters. The simulation results show that the proposed filter has better performance than the extended Kalman filter (EKF) in both robustness and fast convergency.

• 제어로봇시스템학회논문지
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• 제21권9호
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• pp.807-810
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• 2015

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

• 대한조선학회논문집
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• 제40권5호
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• 제어로봇시스템학회논문지
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• 제20권4호
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• pp.468-473
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• 2014

This paper describes the in-flight alignment of SDINS (Strapdown Inertial Navigation Systems) using an EKF (Extended Kalman Filter) and a UKF (Unscented Kalam Filter), which allow large initial attitude error uncertainty. Regardless of the inertial sensors, there are nonlinear error dynamics of SDINS in cases of large initial attitude errors. A UKF that is one of the nonlinear filtering approaches for IFA (In-Flight Alignment) are used to estimate the attitude errors. Even though the EKF linearized model makes velocity errors when predicting incorrectly in case of large attitude errors, a UKF can represent correctly the velocity errors variations of attitude errors with nonlinear attitude error components. Simulation results and analyses show that a UKF works well to handle large initial attitude errors of SDINS and the alignment error attitude estimation performance are quite improved.