• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2352-2360
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• 2007

Due to the friction characteristics of cylinders and the rail of a passenger car, in the elevator actuated with hydraulic systems, there exist dead zones, which can not be controlled by a PID controller. In this paper, the friction characteristics of a cylinder is examined, which may cause the abrupt increase of the acceleration in the zero-costing speed region. To overcome the drawbacks of a PID controlled hydraulic elevator system, a zooming fuzzy logic controller is designed and finally an improved hybrid fuzzy controller is proposed. The effectiveness of the proposed control scheme are shown by simulation and experimental results, which the proposed fuzzy hybrid control method yields good control performance not only in the zero-crossing speed region but also in the overall control region including steady-state region.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.519-520
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• 2016

BLSRM is a nonlinear, strong coupling and multi-variable system. The conventional control method is vulnerable to uncertain factors such as the load disturbance and satellite parameters change. It is difficult to obtain satisfactory control effect. Basing on a 8/10 BLSRM, whose suspending force control is separated with the torque control, this paper presents adaptive fuzzy PID controller for levitation control, which apply the fuzzy logic control to the conventional PID controller for parameters self-tuning. Both fuzzy and parameters of PID controller are self-tuning on-line, which improve the performance of controller. Finally, simulation and experimental results show the performance of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.991-997
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• 2013

In this paper, we propose a CFWC (CCD and fuzzy PID based width control) scheme to obtain the desired delivery width margin of a vertical rolling mill in hot strip process. A WMS(width measurement system) is composed of two line scan cameras, an edge detection algorithm, a glitch filter, and so on. A dynamic model of the mill is derived from a gauge meter equation in order to design the fuzzy PID controller. The controller is a self-learning structure to select the PID gains from the error and error rate of the width margin. The effectiveness of the proposed CFWC is verified from simulation results under a width disturbance of the entry in the mill. Using a field test, we show that the performance of the width control is improved by the proposed control scheme.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1251-1258
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• 1999

The water level control of a steam generator in the unclear power plant is an important process. Most of the water level controllers of the actual plant are PID controllers. But they have limitations in appling for tracking the set point and getting rid of disturbances, so there are some defects to apply in the actual ground even though many research works represented the resolutions to solve it. In this paper, it is suggested that the established simple PID controller in low power has the ability to remove disturbances and trace the set-point, and then possesses the real-time self-tuning function according to the variety of moving peculiarity of a plant. This function realized by making use of fuzzy logic. PID parameters are formulated by a variable ${\alpha}$ and made it fluctuate by a fuzzy inference according to level error and level error change. This mechanism makes application of actual plant effective as well as taking advantage of improving the efficiency of water level controller by way of adding the function of self-tuning instead of replacing PID controller. The computer simulation of this scheme shows the improved performance compare to conventional PID controller.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2545-2547
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• 2000

In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.85-90
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1228-1234
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• 2015

In this paper, a fuzzy controller using thermoelectric for temperature environmental tester is developed. The new structure of fuzzy controller based temperature environmental tester is proposed and implemented to maintain a stable temperature and improve temperature change speed. In order to evaluate the efficiency, an experiment is setup to compare PID controller with our proposed controller. The experimental results, we proved that our proposed fuzzy controller has better performance than PID controller.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.4
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• pp.593-608
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• 1995

In this study two issues are considered, one is to develop a pneumatic servomechanism using a direct-connected circuit between inlet and outlet, the other is to design two kinds of advanced controllers such as fuzzy and PID controllers for a fingering system. Besides, the application of the advanced controllers to the newly proposed servomechanism is presented. The procedure of this study is composed of following 6 steps : [Step 1] Structuring of a control system; [Step 2] Development of a pneumatic circuit for the servomechanism ; [Step 3] Characteristic analysis of the valve and cylinder systems ; [Step 4] Determination of optimal parameters of the PID controller ; [Step 5] Design of a fuzzy controller and parameter tuning; and, [Step 6] Experimental analysis of fuzzy and PID controllers. Experimental results show that the newly proposed pneumatic servomechanism has good performance and, not only the performance of the fuzzy controller is better than that of the PID controller but also the fuzzy controller fits well to the control of the pneumatic servomechanism.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.87-100
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• 1995

In order to control systems which contain nonlinearities of uncertainties, control strategies must deal with the effects of them. Since most of control methods based on system mathematical models have been mainly developed focused on stability robustness against nonlinearities or uncertainties under the assumption that controlled systems are linear time invariant, they have certain amount of limitations to smartly improve the transient responses of systems disturbed by nonlinearities or uncertainties. In this paper, a nonlinear fuzzy PID control method is suggested which can stably improve the transient responses of systems disturbed by nonlinearities, as well as systems whose mathematical characteristics are not perfectly known. Although the derivation process is based on the design process similar to general fuzzy logic controller, resultant control law has analytical forms with time varying PID gains rather than linguistic forms, so that implementation using common-used versatile microprocessors cna be achieved easily and effectively in real-time control aspect.