• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1141-1143
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• 2001

This paper presents a detailed study of the reduced-model extended Kalman filter(EKF) for estimating the rotor speed of an induction motor drive. The general structure of the Kalman filter is reviewed and the various system vectors and matrices are defined. By including the rotor speed as a state variable, the EKF equations are established from a discrete two axis model of the three-phase induction motor, using the software MATLAB/Simulink, simulation of the EKF speed estimation algorithm is carried out for an induction motor drive with indirect vector control.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.683-686
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• 1999

이동로봇은 주행성을 가지며 설정된 이동 경로에 따라 목적지까지 자율적으로 이동하기 위해서는 이동로봇의 실제 위치에 대한 정확한 정보가 확보되어야 한다. 정보확보를 위해서 보통 엔코더, 자이로센서, 비젼센서, 레이저 거리등의 센서를 주로 사용한다. 본 연구에서 주행중인 이동로봇의 위치는 상대센서인 엔코더를 통해 측정된 운동변화량과 출발점에서 이동로봇의 위치로부터 자기유도 주행방법에 의해 계산된다. 이들 상대센서는 이동로봇의 실제 이동에 따라 주행거리 및 주행 방향 변화를 항상 측정할 수 있으므로, 전체 주행구간에 걸쳐 이동로봇의 위치를 연속적으로 측정할 수 있다는 장점이 있으나, 상대센서 측정값에 발생된 오차가 위치 평가값이 연속적으로 누적되므로 실제 위치에 대한 오차가 발생하는 단점이 있다. 즉, 바닥의 미끄럼, 요철, 로봇의 요동(Vibration)등 큰 오차의 요인이 된다. 본 연구에서는 위치를 직접 추정하지 않고 엔코드에서 나온 위치오차, Heading 오차, 자체 엔코드오차 그리고, 자이로 오차와 지자기 센서 오차를 Extended Kalman Filter를 통해 추정하여 이 오차를 다시 위치 계산과 Heading에 되돌려 줌으로서 오차를 보정하는 방법을 제시한다.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1866-1867
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• 2006

In this paper, a robust Extended Kalman filter is proposed by introducing a new sliding mode surface. This filter can be used for the system with a matching condition The new state estimater is designed for stochastic systems with bounded uncertainties

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.6
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• pp.246-256
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• 1986

Determination of an aerodynamic structure is a very important problem in missile modeling. The structure problem is to choose an appropriate set of aerodynamic coefficients to represent chosen missile dynamics. A methodology and criteria to determine a structure from windtunnel data are presented in this paper. Aerodynamic coeffecients in the determined structure are then identified by parameter identification algorithms. The identified coefficients are in turn used to verify appropriateness of the structure. The extended Kalman filter (EKF) and the maximum likelihood mithod (ML) are adopted as the parameter identification algorithm. Both methods exhibit satisfactory results. While the model identified by the ML more closely follows dynamics of the chosen missile than that by the EKF.

• Proceedings of the KIEE Conference
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• 1999.07f
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• pp.2739-2741
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• 1999

The rotor position information is needed to control the speed of SRM(Switched Reluctance Motor). The information of the rotor position have been generally acquired by using the encoder or the resolver. Speed sensors, however, occasionally malfunction under the hostile environment such as EMI, dust, high temperature and humidity, etc. There have been many efforts to drive the motor without speed sensors. In this paper, the EKF(Extanded Kalman Filter) theory is proposed to drive the SRM without speed sensors. Proposed method keeps a robust speed estimation performance against the input noise because it includes a noise model of measuring noise within the system. The validity of the proposed method has been examined by simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.312-315
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• 2003

The paper presents a small. unmanned remote controlled probing ship that can reduce the cost for acquiring data of marine and coastal environments. The control system is composed of three microprocessors. one is for overall mission control. another for control of propulsion motors. and the other for sensor operation. For communication system, we adopt direct and indirect methods based on the wireless modem of commercial cellular telephone. The former is a direct communication between the modems of the ship and the server. and the latter is an indirect communication via internet between the ship and the server. The system is equipped with a digital compass and a GPS system for position estimation, and extended Kalman filter is used for the data association. The performance of the ship is demonstrated with the results produced by sets of experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.766-771
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• 1992

The hydrodynamic coefficients estimation problem is important to develop an underwater vehicle and design a controller for it. In this paper, an identification theory, the Extended Kalman Filter, is applied to this parameter estimation problem. In the case that a process noise is not used, all of the parameters are almost exactly converged to the true values respectively. When a process noise is used, all of the parameters are converged to the true values, too, although some parameter estimates are slightly biased. The comparisons of the two trajectories between those generated by the true parameters and those by the estimated parameters show that the parameter estimation problem is well-solved.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.904-908
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• 2008

Every autonomous system like a robot needs a power source known as a battery. And proper management of the battery is very important for proper operation. To know State of Charge(SOC) of a battery is the very core of proper battery management. In this paper, the SOC estimation problem is tackled based on the well known Extended Kalman Filter(EKF). Combined the existing battery model is used and then EKF is employed to estimate the SOC. SOC table is constructed by extensive experiment under various conditions and used as a true SOC. To verify the estimation result, extensive experiment is performed with various loads. The comparison result shows the battery estimation problem can be well solved with the technique proposed in this paper. The result of this paper can be used to develop related autonomous system.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.4 s.23
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• pp.14-23
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• 2005

In this paper we propose a sensor fusion method for the navigation algorithm which can be used to estimate state vectors such as position and velocity for its motion control using multi-sensor output measurements. The output measurement we will use in estimating the state is a series of known multi-sensor asynchronous outputs with measurement noise. This paper investigates the Extended Kalman Filtering method to merge asynchronous heading, heading rate, velocity of DVL, and SSBL information to produce a single state vector. Different complexity of Kalman Filter, with. biases and measurement noise, are investigated with theoretically data from MOERI's SAUV. All levels of complexity of the Kalman Filters are shown to be much more close and smooth to real trajectories then the basic underwater acoustic navigation system commonly used aboard underwater vehicle.