• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.4
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• pp.18-27
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• 2002

Aerodynamic shape optimization of two-dimensional airfoils in inviscid compressible flows is performed using a continuous adjoint formulation on unstructured meshes. Accurate evaluation of the gradient is achieved by using a reconstruction scheme based on the Laplacian averaging. A least-square method with extended stencil is used for flow gradient calculations. Proper convergence criterion is studied on Euler and adjoint equations for efficient design. The present method has been applied to RAE2822 and NACA0012 airfoils such that wave drag can be minimized by removing the shock wave. An inverse design is also performed to recover the shock wave on the designed RAE2822 airfoil.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.191-196
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• 2004

This study proposed the analysis of mass position detection and modified stiffness due to the change of the mass and stiffness of structure by using the original and modified dynamic characteristics. The method is applied to examples of a cantilever and 3 degree of freedom by modifying the mass. The predicted detection of mass positions and magnitudes are in good agrement with these from the structural reanalysis using the modified mass.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.344-348
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• 2010

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.332-336
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• 2010

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.2
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• pp.1-10
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• 1996

The objective of this paper is to develop a motion base for the vehicle driving simulator. Kinematic analysis are carried out to obtain maximum strokes and velocities of hydraulic actuators. Hydraulic control forces of the actuators are estimated by inverse dynamic analysis. Finally, an optimal design is performed to find attachment points of the actuators so that control forces are minimized. A control logic for the motion base is developed to make the motion base follow the given reference signals. The control logic is implemented on a digital signal processor(DSP) board.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.393-402
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• 1990

An iterative learning contol method is presented for a class of linear periodic systems, in which a parameter estimator of the system together with an inverse system model is utilized to generate the control signal at each iteration. A convergence proof is given and two numerical examples are illustrated to show the validities of the algorithm. In particular, it is shown that the method is useful for the continuous path control of robot manipulators.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.654-659
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• 1993

In this paper, a robot controller that has a real time-multitasking OS (Operating System) is developed. It can do given jobs in realtime, so its effectiveness is increased. The controller has several CPU boards, and it is needed to communicate among these boards. For that reason, it is adopted VME bus system and VMEexec OS that can process multiprocess in realtime. Multiprocess includes robot language edit process, vision process, low level motion control process, and teach process in higher layer. And dynamics, kinematics, and inverse kinematics that require realtime calculation are included in lower layer.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.241-244
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• 1996

This paper discusses the general properties and the design procedures of Internal Model Control(IMC) scheme for nonlinear plants. Also we propose new nonlinear IMC(NIMC) design method using linear IMC. Although all IMC controllers can be thought simple 'inverse controller', its nonlinear realization is not easy. Propose NIMC is composed multiple linear models, IMC controllers, and switching scheme. The advantages of this method are we can use simple linear IMC design method and need not nonlinear modelings.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1209-1212
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• 1996

In this paper, we design a stabilizing controller with disturbance rejection properties for multirate sampled-data systems which have periodic output measurement scheme. We assume that the plant is open-loop unstable and the disturbance consists of a sum of finite number of sine waves with different frequencies. A sufficient condition for maintaining observability in multirate sampled-data systems is derived. The proposed controller has an IMC structure and can be decomposed into the filtered-disturbance estimator and the inverse of the fast uniform sampled model of the pre-stabilized plant. An example is presented for illustrations.