• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1115-1119
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• 2007

Five control methods (Speed Control, PID Gain Scheduling, Loop Time Control, Simple PID, Switching Control) have been applied to the control of an Inline Co-axial valve by the simulation of AMESim. The simulation results have shown that the speed control method is the most stable and the fastest way to reach to the set point in the simulation of the flow control. Moreover, It has been found that the five control methods have the almost same characteristics in the power consumption, the counter electromotive force, and the motor angular velocity. According to the analysis results, the fast and stable control characteristics of the speed control method is the most suitable for the flow control using a inline co-axial valve with a DC(BLCD) motor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1366-1369
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• 1993

This paper presents a simulation study on two self-learning control systems for a fuzzy prediction model of CO (carbon monoxide) concentration:linear control and fuzzy control. The self-learning control systems are realized by using Widrow-Hoff learning rule which is a basic learning method in neural networks. Simulation results show that the learning efficiency of fuzzy controller is superior to that of linear controller.

• Journal of Aerospace System Engineering
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• v.14 no.4
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• pp.10-17
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• 2020

When an aircraft is taxiing, excitation force is applied according to the shape of the road surface. The sprung mass acceleration caused by the excitation of the road surface negatively affects the feeling of boarding. This paper addresses the verification process of the semi-active control method applied to improve the feeling of boarding. The Magneto-Rheological damper landing gear model is employed alongside the control method. It is a Oleo-Pneumatic damper filled with a fluid having the characteristics of increasing yield stress when subjected to a magnetic field. The control method involves verifying Skyhook Control Type2 developed by Skyhook control. The Sinozuka white noise model that considers runway characteristics was employed for the road surface in the simulation. The runway road surface obtained through this model has stochastic characteristics, so the dynamic characteristics were analyzed by applying Monte-Carlo simulation. A dynamic analysis was conducted by co-simulating the landing gear model made by RecurDyn and the control method designed by Simulink. Simulation results show that the Skyhook Control Type2 method has the best control effect in the low speed range compared to the passive type (without control) and skyhook control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1489-1495
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• 2012

In this study, the active noise control experiment has been performed using induction motor noises. While the noises were measured, a induction motor was operated in different speed. For the simulation of ANC(Active Noise Control), test-bed is composed a multi-channel ANC system was constructed. In order to compare the control performance, we performed noise reduction simulations of ANC by Co-FXLMS algorithm and FXLMS algorithm. Through the simulation results, we confirmed that convergence performance and noise decrease effect of the proposed Co-FXLMS algorithm have been improved from existing FXLMS algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.472-475
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• 2003

Electric Power Steering (EPS) mechanism has become widely equipped in passenger vehicle due to the environmental consciousness and higher fuel efficiency. This paper describes the development of co-simulation technique and simulation integration technique of EPS control system with dynamic vehicle model. A full vehicle model interacted with EPS control algorithm is concurrently simulated on a single bump road condition. Dynamic responses of vehicle chassis and steering system resulting from road surface impact are evaluated and compared with proving ground experimental data. The comparisons will show reasonable agreement on tie-rod load. rack displacement, handle-wheel torque and tire center acceleration. This developed simulation capability can be used for EPS performance evaluation and calibration as well as for vehicle handling performance integration and synthesis.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.7 s.196
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• pp.26-32
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• 2007

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1028-1032
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• 1988

The purpose of this paper is the development of a fully integrated simulation package for industrial robot. The simulation package consists of kinematics, dynamics, and control. The kinematics contains trajectory plans and inverse kinematics. The dynamics combines manipulator dynamics and actuator dynamics including the effect of payloads and viscous frictions. The control is a hardware oriented scheme which contains position controller, velocity controller, current controller, and PWM generator. Thus, the simulation package can be used not only for theoretical purposes but also for development purposes in industry. Using this package, the characteristics and performances of the SCARA robot, which has been developed in GSIS, are investigated.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.166-171
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• 2003

This paper presents the algorithms and Matlab Toolbox for co-simulation of distributed real-time control system based on OSEK-OS and CAN protocol. This toolbox enables the developers to analyze the timing uncertainty, which is caused by resource sharing including shared memories and networks, and to take the timing uncertainty into consideration in the early design phase. Furthermore, this toolbox helps the developers to model the behaviors of a control system by providing graphical user interface for objects of OSEK-OS and CAN. To prove the feasibility of this toolbox, a vehicle body network system is modeled with this toolbox, and the timing uncertainties are analyzed.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.141-148
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• 2008

In this paper, we develop a detailed full dynamic model which includes various rough terrains for 6-wheel skid-steering mobile robot based on the real experimental autonomous vehicle called Dog-Horse Robot. We also design a co-simulation for performance comparison of path tracking algorithms. The control architecture in the co-simulation can be divided into two levels. The high level control is the closed-loop control of path tracking to follow a given path, and the low level is concerned about torque control of wheel motion. The simulation using the mechanical data of the Dog-Horse Robot is performed under the Matlab/Simulink environment. We also simulate and evaluate the performance of the model based adaptive controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.737-742
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• 2007

The method of the reducing duct noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequencies (below 500Hz) and is limited by the space. On the other hand, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases slightly so it may delay the convergence time when the FXLMS algorithm is applied to the active control of duct noise. Thus the Co-FXLMS algorithm was developed to improve the control performance in order to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing duct noise.