• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.4
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• pp.451-458
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• 2012

In this paper, the design study of a new convertible six d.o.f underwater robot which can be a ROV or AUV according to underwater work purpose is presented. A structure design about the ROV and the AUV and its design on the control system is presented. In case of the AUV, an analysis on thruster forces in accordance with operating speed has been performed. A sensor fusion board which can proceed various sensor signals to identify correct positions and speeds has been developed and a total control system including EKF(Extended Kalman Filter) has been designed and developed.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.5
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• pp.471-481
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• 2015

This paper presents the robot vision control schemes using Extended Kalman Filter (EKF) method for the slender bar placement in the appearance of obstacles during robot movement. The vision system model used for this study involves the six camera parameters($C_1{\sim}C_6$). In order to develop the robot vision control scheme, first, the six parameters are estimated. Then, based on the estimated parameters, the robot's joint angles are estimated for the slender bar placement. Especially, robot trajectory caused by obstacles is divided into three obstacle regions, which are beginning region, middle region and near target region. Finally, the effects of number of obstacles using the proposed robot's vision control schemes are investigated in each obstacle region by performing experiments of the slender bar placement.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.74-81
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• 2005

A 3-D localization method of an autonomous underwater vehicle (AUV) has been developed, which can solve the limitations oj the conventional localization, such as LBL or SBL that reduces the flexibility and availability of the AUV. The system is composed of a mother ship (small unmanned marine prober) on the surface of the water and an unmanned underwater vehicle in the water. The mother ship is equipped with a digital compass and a GPS for position information, and an extended Kalman filter is used for position estimation. For the localization of the AUV, we used only non-inertial sensors, such as a digital compass, a pressure sensor, a clinometer, and ultrasonic sensors. From the orientation and velocity information, a priori position of the AUV is estimated by applying the dead reckoning method. Based on the extended Kalman filter algorithm, a posteriori position of the AUV is, then, updated by using the distance between the AUV and a mother ship on the surface of the water, together with the depth information from the pressure sensor.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.803-806
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• 2003

This paper presents a detailed study of the extended Kalman filter for estimating the rotor speed of an IM 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 direct self control. The investigations show that the EKF is capable of tracking the actual rotor speed provided that the elements of the covariance matrices are properly selected.

• 정보저장시스템학회:학술대회논문집
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• 2005.10a
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• pp.44-46
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• 2005

Data storage related with writing and retrieving requires high storage capacity, fast transfer rate and less access time. Today any data storage system cannot satisfy these conditions, however holographic data storage system can perform faster data transfer rate because it is a page oriented memory system using volume hologram in writing and retrieving data. System can be constructed without mechanical actuating part therefore fast data transfer rate and high storage capacity about $1Tb/cm^3$ can be realized. In this paper, to reduce errors of binary data stored in holographic data storage system, a new method for bit error reduction is suggested. We proposal Algorithm use The Extended Kalman filter. The Kalman filter reduce measurement noise. Therefore, By using this error reduction method following results are obtained; the effect of measurement nois of Pixel is decreased and the intensity profile of data page becomes uniform therefore the better data storage system can be constructed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.2
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• pp.166-176
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• 2000

In this paper, the LEO system signal degradation is mainly due to fading and doppler shift, so that the analysis of the signal degradation and compensation techniques are very important. This paper propose a Kalman filter based two step Automatic Frequency Control(AFC) to combat large and time variant frequency offset in low earth orbit satellite communication systems. The proposed Kalman AFC method estimates a frequency offset in two steps, I. e., coarse and fine estimations, extending the frequency acquisition range to even for than the symbol rate. Furthermore, it can track well a time variation of frequency offset. It is shown that the proposed compensator is able to compensate for doppler shift more than several KHz.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.9-16
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• 1999

As a part of target motion analysis(TMA) with highly noisy bearings-only measurements from a passive sonar system, a nonlinear batch estimator is proposed to provide the initial estimates to a sequential estimator called the modified gain extended Kalman filter(MGEKF). Based on the system observability analysis of passive target tracking, a practical and effective method is suggested to determine the observer maneuvers for improved TMA performance through system observability enhancing. Also suggested is a method to determine observer location for enhanced system observability at the initial phase of TMA from various engagement boundaries which represent the relationship between observer-target relative geometrical data and system observability. The proposed TMA methods are tested by a series of computer simulation runs.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.50-58
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• 2009

For precise tracking control of a servo system with nonlinear friction, a robust friction compensation scheme is presented in this paper. The nonlinear friction is difficult to identify the friction parameters exactly through experiments. Friction parameters can be also varied according to contact conditions such as the variation of temperature and lubrication. Thus, in order to overcome these problems and obtain the desired position tracking performance, a robust adaptive back-stepping control scheme with a dual friction observer is developed. In addition, to estimate lumped friction uncertainty due to modeling errors, a DEKF recurrent neural network and adaptive reconstructed error estimator are also developed. The feasibility of the proposed control scheme is verified through the experiment fur a ball-screw system.