• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.668-671
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• 1996

Two expressions for the inertial navigation system error equations are derived using a perturbation method; one in navigation frame, and the other in geographic frame. The equivalence between two expressions is shown by explicit equations and computer simulation.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제21권4호
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• pp.203-214
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• 2017

In this paper we propose and analyze two a posteriori error estimators for the stabilized $P_1/P_1$ finite element discretization of the Stokes equations. These error estimators are computed by solving local Poisson or Stokes problems on elements of the underlying triangulation. We establish their asymptotic exactness with respect to the velocity error under certain conditions on the triangulation and the regularity of the exact solution.

• 제어로봇시스템학회논문지
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• 제12권6호
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• pp.608-613
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• 2006

Any input acceleration that bends RLG dithering axis causes flexure error, which is a source of the noncommutative error that can not be compensated by simply using integrated gyro sensor output. This paper introduces noncommutative error equations that define attitude errors caused by flexure errors. In this paper, flexure error is classified as sensor level error if the sensing axis coincides with the dithering axis and as system level error if the two axes do not coincide. The relationship between gyro output and the rotation vector is introduced and is used to define the coordinate transformation matrix and angular motion. Equations are derived for both sensor level and system level flexure error analysis. These equations show that RLG based INS attitude error caused by flexure is directly proportional to time, amount of input acceleration and the dynamic frequency of the vehicle.

• 제어로봇시스템학회논문지
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• 제9권7호
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• pp.556-562
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• 2003

This paper presents new attitude error differential equations to define attitude errors as the rotation vector for inertial navigation systems. Attitude errors are defined with the rotation vector between the reference coordinate frame and the platform coordinate frame, and Platform dynamics to the reference coordinate frame due to platform torque command errors are defined. Using these concepts for attitude error definition and platform dynamics, we have derived attitude error differential equations expressed in original nonlinear form for GINS and SDINS and showed that these are equivalent to attitude error differential equations expressed in known linear form. The relation between attitude errors defined by the rotation vector and attitude errors defined by quaternion is clearly presented as well.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2012년도 춘계학술대회
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• pp.160-161
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• 2012

This paper is to develop & analyze the position & direction error equations in the free-gyro positioning & directional system by using two free gyros and is to find out the amount of the errors. First, the position & direction error equations are introduced and developed, based on the position & direction equations. Second, the value of errors is discussed based on sensors errors.

• 제어로봇시스템학회논문지
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• 제20권2호
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• pp.149-156
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• 2014

A magnetic compass module must be calibrated accurately before use. Moreover, the calibration process must be performed taking into account any magnetic dip if the magnetic compass module has tilt angles. For this, a calibration method for a magnetic compass module is explained. Tilt error of the magnetic compass module is compensated using a biaxial accelerometer generally. The accelerometer error causes a tilt angle calculation error that gives rise to an azimuth calculation error. For error property analysis, error equations are derived and simulations are performed. In the simulation results, the accuracy of derived error equations is verified. If a biaxial magnetic compass module is used instead of a triaxial one, the magnetic dip and z-axis magnetic compass data must be estimated for tilt compensation. Lastly, estimation equations for the magnetic dip and z-axis magnetic compass data are derived, and the performance of the equations is verified based on a simulation.

• 한국항해항만학회지
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• 제37권4호
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• pp.329-335
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• 2013

This paper is to develop the position error equations including the attitude errors, the errors of nadir and ship's heading, and the errors of ship's position in the free-gyro positioning and directional system. In doing so, the determination of ship's position by two free gyro vectors was discussed and the algorithmic design of the free-gyro positioning and directional system was introduced briefly. Next, the errors of transformation matrices of the gyro and body frames, i.e. attitude errors, were examined and the attitude equations were also derived. The perturbations of the errors of the nadir angle including ship's heading were investigated in each stage from the sensor of rate of motion of the spin axis to the nadir angle obtained. Finally, the perturbation error equations of ship's position used the nadir angles were derived in the form of a linear error model and the concept of FDOP was also suggested by using covariance of position error.

• Journal of applied mathematics & informatics
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• 제7권2호
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• pp.409-438
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• 2000

In this paper we develop a new procedure to control stepsize for Runge- Kutta methods applied to both ordinary differential equations and semi-explicit index 1 differential-algebraic equation In contrast to the standard approach, the error control mechanism presented here is based on monitoring and controlling both the local and global errors of Runge- Kutta formulas. As a result, Runge-Kutta methods with the local-global stepsize control solve differential of differential-algebraic equations with any prescribe accuracy (up to round-off errors)

• 대한수학회보
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• 제34권1호
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• pp.43-49
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• 1997

In this paper, we study an error of vanishing viscosity method for the first-order Hamilton-Jacobi equations.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제28권1호
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• pp.33-58
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• 2024

This paper reviews basic concepts for Physics-Informed Neural Networks (PINN) applied to the initial value problems for ordinary differential equations. In particular, using only basic calculus, we derive the error estimates where the error functions (the differences between the true solution and the approximations expressed by neural networks) are dominated by training loss functions. Numerical experiments are conducted to validate our error estimates, visualizing the relationship between the error and the training loss for various first-order differential equations and a second-order linear equation.