• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.11a
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• pp.130-134
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• 2001

In the re-entry control system, errors apt to induce because the time derivative of drag acceleration is analytically estimated. Still more, the difficulty of estimation of the exact drag coefficient in hypersonic velocity and the nun-reality of the scale height cause a steady-state drag error. This paper proposes the additional method of the disturbance observer. This reduces the steady-state drag error according to the following series. First, this method estimates a error in drag acceleration time derivative by the analytic calculation and then creates the new drag acceleration time derivative using the estimated error. The performance of the re-entry control system is verified about 32 reference trajectories.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.803-812
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• 2000

In this paper we introduce a model based centralized hierarchical controller for cooperative team of soccerplaying multiple mobile robots. The hierarchical controller is composed of high-level and low-level controllers. Using the coordinates information of objects from the vision are simple models of multiple mobile tobots on the playground. Subsequently, the high level controller selects and action model corresponding to the perceived state transition model and generates subgoal and goal-velocity, from which the low level controller generates trajectory of each wheel velocity of the robot. This two layered simplicity. The feasubility of the control strategy has been demonstrated in an implementation for real soccer games at a MiroSot league.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.51-53
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• 1993

This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2308-2310
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• 2001

In the adaptive fuzzy sliding mode control, from a set of a fuzzy IF-THEN rules adaptive fuzzy sliding mode control whose parameters are adjusted on-line according to some adaptation laws is constructed for the purpose of controlling the plant to track a desired trajectory. Most of the research works in nonlinear controller design using fuzzy systems consider the affine system with fixed grid-rule structure based on system state availability. The fixed grid-rule structure makes the order of the controller big unnecessarily, hence the on-line fuzzy rule structure and fuzzy observer based adaptive fuzzy sliding mode controller is proposed to solve system state availability problems. Therefore, adaptive laws of fuzzy parameters for state observer and fuzzy rule structure are established implying whole system stability in the sense of Lyapunov.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.34-39
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• 2015

In this paper, a finite impulse response (FIR) filter is proposed for discrete-time nonlinear systems. The proposed filter is designed by combining the estimate of the perturbation state and nominal state. The perturbation state is estimated by adapting the optimal time-varying FIR filter for the linearized perturbation model and the nominal state is directly obtained from the nonlinear nominal trajectory model. Since the FIR structured estimators use the finite horizon information on the most recent time interval, the proposed extended FIR filter satisfies the bounded input/bounded output (BIBO) stability, which can't be obtained from infinite impulse response (IIR) estimators. Thus, it can be expected that the proposed extended FIR filter is more robust than IIR structured estimators such as an extended Kalman filter for the round-of errors and the uncertainties from unknown initial states and uncertain system model parameters. The simulation results show that the proposed filter has better performance than the extended Kalman filter (EKF) in both robustness and fast convergency.

• Journal of Magnetics
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• v.22 no.1
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• pp.49-54
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• 2017

The significant reduction of the image quality caused by the magnetic field of samples is a major problem affecting the application of SEM (scanning electron microscopy) in the analysis of electronic devices. The main reason for this is that the electron trajectory is deflected by the Lorentz force. The usual solution to this problem is degaussing the sample at high temperatures. However, due to the poor heat resistance of some electronic components, it is imperative to find a method that can reduce the impact of magnetic field on the image quality and is straightforward and easy to operate without destroying the sample. In this paper, the influence of different magnetic field directions on the imaging quality was discussed by combining the experiment and software simulation. The principle of the method was studied, and the best observation direction was obtained.

• Bulletin of the Korean Chemical Society
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• v.24 no.6
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• pp.837-842
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• 2003

In ab initio molecular dynamics, whenever information about the potential energy surface is needed for integrating the equations of motion, it is computed “on the fly” using electronic structure calculations. For Born-Oppenheimer methods, the electronic structure calculations are converged, whereas in the extended Lagrangian approach the electronic structure is propagated along with the nuclei. Some recent advances for both approaches are discussed.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.324-326
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• 2006

This paper present backstepping control approach for controling chaotic Liu system. The proposed method is a systematic design approach and consists in a recursive procedure that interlaces the choice of a Lyapunov Function. Based on Lyapunov stability theory, control laws are derived. We used the same technique to enable stabilization of chaotic motion to a steady state as well as tracking of any desired trajectory to be achieved in a systematic way. Numerical solution are shown to verify the result.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.70-74
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• 1989

To track the target trajectory with maneuvers, unknown maneuvering inputs must be estimated. To do this the direct estimation algorithm using generalized least square technique is developed based on the procedure of failure detection and identification(FDI) theory. Through the simulation using maneuvering target scenario, tracking performance and efficiency of the algorithm developed here are investigated.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1140-1145
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• 1989

A general dynamic iterative learning control scheme is proposed for a class of nonlinear systems. Relying on stabilizing high-gain feedback loop, it is possible to show the existence of Cauchy sequence of feedforward control input error with iteration numbers, which results in a uniform convergance of system state trajectory to the desired one.