• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1504-1508
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• 1996

In this paper, we derive dynamic equation of helicopter and design controller based on variable structure system. It is difficult to control helicopter because it has non-linear coupling between input and output of system and is MIMO system. The design of control law is considered here using variable structure methodology giving the robustness to parameter variations and invariance to some subsets of external disturbance. However we derive dynamic equations of helicopter and design stabilizing variable structure controller. Also, simulation results are given in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.5-176
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• 2001

This paper presents a fuzzy dynamic output feedback controller design method for Parallel Distributed Compensation (PDC)-type Takagi-Sugeno (T-S) model based fuzzy dynamic system with H$\infty$ performance and additional constraints on the closed pole placement. Design condition for these controller is obtained in terms of the linear matrix inequalities (LMIs). The proposed fuzzy controller satisfies the disturbance rejection performance and the desired transient response. The design method is verified by this method for an inverted pendulum with a cart using the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.965-970
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• 1992

A dynamic modeling and analysis for the Stewart platform type of parallel robot is addressed. The dynamic modeling is performed based on the method of Kinematic Influence Coefficients(KIC) and transfering of the generalized coordinates. The optimum geometric configurations of the system that minimize the actuating forces at the linear actuator are found for several trajectories by using the optimization technique.

• Advances in nano research
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• v.4 no.4
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• pp.265-279
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• 2016

Present study interests with the longitudinal forced vibration of nanorods. The nonlocal elasticity theory of Eringen is used in modeling of nanorods. Uniform, linear and sinusoidal axial loads are considered. Dynamic displacements are obtained for nanorods with different geometrical properties, boundary conditions and nonlocal parameters. The nonlocal effect increases dynamic displacement and frequency when compared with local elasticity theory. Present results can be useful for modeling of the axial nanomotors and nanoelectromechanical systems.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1736-1737
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• 2007

Significant increase of container flows in marine terminals requires more efficient automatic port systems. This paper presents a novel routing and collision avoidance algorithm of linear motor based shuttle cars using random access sequence dynamic programming (RAS DP). The proposed RAS DP is accomplished online for determining optimal paths for each shuttle car.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1202-1206
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• 2001

The paper presents the dynamic stability of a vertical cantilevered pipe conveying fluid and having translational linear spring supports. Real pipe systems may have some elastic hanger supports or other mechanical attached parts., which can be regarded as attached spring supports. Governing equations are derived by energy expressions, and numerical technique using Galerkin's method is applied to discretize the equations of small motion of the pipe. Effects of spring supports on the dynamic stability of a vertical cantilevered pipe conveying fluid are fully investigated for various locations and spring constants of elastic supports.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.82-85
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• 1987

The objective of this paper is to develop computer programs to aid in the design and analysis of control systems in which nonlinear characteristics exist. Control systems are dynamic systems, which can be described using various mathematical models. A convenient model for digital computer simulation is the state model in which described using a set of linear and non linear first order differential equations. The digital simulation was performed on a IBM PC/XT personal computer, and the computer language was BASIC. There are four possible configurations from which a user may choose. When running a program, the user is asked to enter the system parameters according to a specified control system configurations are; 1. A control system with a nonlinear element followed by a plant in a feedback configurations(NLSVF1). 2. A control system with a nonlinear device situated between two plants in a feedback configurations(NLSVF2). 3. A control system with a nonlinear element followed by a plant, followed by a the dealy in feedback configurations(TLAG). 4. A motor and load with a backlash nonlinearity between dynamic portions of the motor/load configurations (BACKLASH). The matrix from state equations are integrated using combination the trapezoidal method and fixed point iteration. Several cases which have nonlinearity were implemented on the computer and the results were discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.295-300
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• 1998

In this paper, we point out the possibility of the divergence of control input caused by the estimation error of delay-time when general iterative learning algorithms are applied to a class of linear dynamic systems with time-delay in which delay-time is not exactly measurable, and then propose a new type of iterative learning algorithm in order to solve this problem. To resolve the uncertainty of delay-time, we propose an algorithm using holding mechanism which has been used in digital control system and/or discrete-time control system. The control input is held as constant value during the time interval of which size is that of the delay-time uncertainty. The output of the system tracks a given desired trajectory at discrete points which are spaced auording to the size of uncertainty of delay-time with the robust property for estimation error of delay-time. Several numerical examples are given to illustrate the effeciency of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.188-188
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• 2000

The most important problem in target tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters baged on the dynamic equations. In this paper, we propose the extended robust Kalman filter (ERKF) which can be applied to the real target tracking system with the parameter uncertainties. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter (EKF) via 3-dimensional target tracking example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1021-1024
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• 1993

System identification is a major component for a control system. In biosystems, which is nonlinear and dynamic, precise identification would be very helpful for implementing a control system. It is difficult to precisely identify such non-linear systems. The measurable data on products from 2,3-butanediol fermentation could not be included in a process model based on kinetic approach. Meanwhile, a predictive capability is required in developing a control system. A neural network (NN) dynamic identifier with a by/(1＋ t ) transfer function was therefore designed being able to predict this fermentation. This modified inverse NN identifier differs from traditional models in which it is not only able to see but also able to predict the system. A moving window, with a dimension of 11 and a fixed data size of seven, was properly designed. One-step ahead identification/prediction by an 11-3-1 BPNN is demonstrated. Even under process fault, this neural network is still able to perform several-step ahead prediction.