• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.108.4-108
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• 2001

The integrated environment for the fuzzy logic controller with the acceleration converter to improve the dynamic characteristic of a one-axis moving table is proposed in the paper. The two inputs of the fuzzy logic controller are position error and velocity. The two inputs of the acceleration converter used to produce the acceleration input for the one-axis moving table are actual and setting rotational speeds. The proposed controller is implemented on a personal computer by using MATLAB and Visual Basic for a real time control via a motion control board. The position control results when the stair input is applied to control the position of the one-axis moving table, are shown. The experimental results of the proposed position control system are compared to the conventional PID ...

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.216-223
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• 2016

This paper proposes a new controller design method for a fork-type lifter (FTL) of a transportation mobile robot. The transportation robot needs to pick up a package from a stack on a storage shelf and move on by a planned path in a logistics center environment. The position of the storage shelf is recognized by reading a QR code on the floor, and using this position, the robot can move to reach the storage shelf and pick up the package. PID controllers and an adaptive controller are designed to control the velocity of two wheels and the position of the FTL. An adaptive controller for the lifter is designed to elevate up and down on a slideway to the correct height position of the package on the stack of the storage shelf. The simulation results show that the PID controllers can respond smoothly to the desired angular velocity and the adaptive controller can adapt quickly and correctly to the desired height.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.830-832
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• 1995

A position Control algorithm of the flexible manipulator is studied. The proposed algorithm is based on a Fuzzy Logic Control(FLC) method using the human's experiences. FLC does not need a dynamic modeling of a flexible manipulator. A Fuzzy logic controller is designed that the end-point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by the error and variation of error. Simulation result shows a robustness of FLC compared with the PID control algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.185-190
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• 2007

In recent days, vibration isolation system is mostly required in precise measurement and manufacturing system to reduce vibration due to external disturbances and internal actuators. Among all the vibration isolation systems, air spring is widely used because of its low resonant frequency and high damping ratio. In this study, we first analyze the passive air-spring system using leveling valve, and then design the active vibration isolation system. Because the non-linearity of pneumatic characteristics, we try to design the fuzzy controller which is better than PID controller at complex and non-linear system, and then compare them both in experiment and simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.525-532
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• 2004

Telemanipulator is distingushed from industrial robot by iterating same specified work. Manipulator operator is included in control loop for controlling the telemanipulator because he decide directly during the work and order controllabily. We implement fuzzy controller for reducing the modelling error of telemanipulator which depend on the PID controller. But position-force control method of bidirectional control impose unsafety of vibiration and Analytic Hierchy method can stabilize for reducing nonlinear modelling error by expert operator because of transformation empirical control rule to linear model.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.187-192
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• 2004

In this paper, we proposed the method to design fuzzy controller using the experience of the operating expert and experimental numeric data for the robust control about the noise and disturbance instead of the traditional PID controller for the main steam temperature control of the thermal power plant. The temperature of main steam temperature process has to be controlled uniformly for the stable electric power output. The process has the problem of the hunting for the cases of various disturbances. In that case, the manual action of the operator happened to be introduced in some cases. We adopted the TSK (Takagi-Sugeno-Kang) model as the fuzzy controller and designed the fuzzy rules using the informations extracted directly from the real plant and various operating condition to solve the above problems and to apply practically. We implemented the real fuzzy controller as the Function Block module in the DCS(Distributed Control System) and evaluated the feasibility through the experimental results of the simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.691-695
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• 1998

Most of systems has nonlinearity . And also accurate modelings of these uncertain nonlinear systems are very difficult. In this paper, a fuzzy modeling technique for the stabilization control of an IP(inverted pendulum) system with nonlinearity was proposed. The fuzzy modeling was acquired on the basis of ANFIS(Adaptive Neuro Fuzzy Infernce System) which could learn using a series of input-output data pairs. Simulation results showed its superiority to the PID controller. We believe that its applicability can be extended to the other nonlinear systems.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.632-634
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• 1998

This paper presents experimental results of the fuzzy controller for DC servo motor. The fuzzy controller consists of 9 quantized levels and 25 fuzzy rules. The fine Controller is employed in the fine control mode when the value of error is between -0.03 and +0.03, whereas the coarse controller is used in the coarse control mode when the value of error is in the outside range of -0.03 and +0.03. The experimental results show that the fuzzy controller provides a better performance (lower overshoot and error) than the PID controller regardless of the load applied.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.316-318
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• 1993

In order to estimation the optimum PID parameter of the turbine speed controller, the response cure of the object plant was compared with the reference pattern and then the magnitude peak value error and peak time error was calculated. With the calculated errors as input into the Fuzzy inference Method was introduced to propose the tuning method for each parameter. And the computer simulation was performed with the above Fuzzy inference method in which the Chunju hydro power plant turbine governor system was used as a model. This Study also aims to develop the exclusive tuner for govenor using industrial computer.