• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.57-64
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• 1981

This paper develops a method of modifying the usual maximum likelihood estimators of the parameters of a two state Markov process when the trajectory of the process can only he observed at regular epochs. The method utilizes the limiting behaviors of the process and the properties of state transition counts. An efficient adaptive strategy to be used together with the modified estimator is also proposed. The properties of the new estimators and the adaptive strategy are investigated using Monte Carlo simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.845-849
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• 1992

The disturbance and time delay can often cause a significant error in the estimation of trajectory of a underwater vehicle. The time delay considered in this study is due to the delayed rudder response to the rudder input from the guidance control part. The simulation tests are performed on maneuver with constant rudder angle, zigzag maneuver, dive-climb maneuver, and corridor pattern maneuver. The results are compared with those of without delay cases.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2898-2900
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• 2000

A sliding mode control algorithm combined with an adaptive scheme, which is used to estimate the unknown parameter bounds. is developed for the trajectory control of robot manipulators. Simulated results show the validity to accurate tracking capability and robust performance.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1664-1673
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• 2016

Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative $LiCoO_2/LiFePO_4/LiNiMnCoO_2$ cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the $LiFePO_4$ cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

• Journal of Power System Engineering
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• v.12 no.2
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• pp.55-61
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• 2008

This paper discusses the advantages of using Fourier-based periodic excitation and of combining internal and external models in dynamic robot parameter identification. Internal models relate the joint torques or forces with the motion of the robot; external models relate the reaction forces and torques on the bedplate with the motion data. This combined model allows to combine joint torque/force and reaction torque/force measurements in one parameter estimation scheme. This combined model estimation will yield more accurate parameter estimates, and consequently better predictions of actuator torque, which is shown by means of a simulated experiment on a CRS A465 industrial robot.

• The Journal of Korea Robotics Society
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• v.2 no.4
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• pp.310-316
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• 2007

The paper proposes a human-following behavior of mobile robot and an intelligent space (ISpace) is used in order to achieve these goals. An ISpace is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to track a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to track the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and trackinging of the walking human with the mobile robot are presented.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.51-60
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• 2007

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle(UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.132-140
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• 1999

This paper treats the estimation of human impedance and their application to human-robot collaboration work. Initially, we perform an experiment at which the human becomes a slave and the robot behaves like a master having F/T sensor on its end. The human impedance expressed in terms of mass, damping, and stiffness properties are estimated based on the force data measured by F/T sensor and the commanded position data of the robot. To show the effectiveness of the estimated human impedance, we perform the second experiment at which the roles of the human and the robot are reversed. It is shown that the robot using the estimated human impedance follows the trajectory commanded by human very smoothly.

• Korean Journal of Child Studies
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• v.27 no.3
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• pp.169-187
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• 2006

A hierarchical linear model(HLM) provides advantages over existing traditional statistical methods (e.g., ordinary least squares regression, repeated measures analysis of variance, etc.) for analyzing multilevel/longitudinal data or diary methods. HLM can gauge a more precise estimation of lower-level effects within higher-level units, as well as describe each individual's growth trajectory across time with improved estimation. This article 1) provides scholars who study children and families with an overview of HLM (i.e., statistical assumptions, advantages/disadvantages, etc.), 2) provides an empirical study to illustrate the application of HLM, and 3) discusses the application of HLM to the study of children and families. In addition, this article provided useful information on available articles and websites to enhance the reader's understanding of HLM.

• Journal of Hydrospace Technology
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• v.1 no.1
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• pp.66-74
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• 1995

Unknown parameters can be determined by system identification techniques. Extended Kalman filter method was introduced as a real time estimator of hydrodynamic derivatives but it has the problem named the coefficient drift. In this study, 2nd order filter estimates hydrodynamic derivatives in Abkowitz model In order to reduce the coefficient drift, parallel processing is used. The measured state and ship trajectory are compared with the estimated values. Parallel processing of 2nd order filter gives very similar results to parallel processing of extended Kalman filter. Parallel processing cannot not remove the coefficient drift perfectly, but it reduces the estimation error.