• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.206-208
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• 1997

This paper presents a design of self_tuning fuzzy logic controller using Genetic Algorithms for power system stabilization. FPSS(Fuzzy Logic Power System Stabilized is applied to the KERI(Korea Electric Research Int.) power system simulator so that its efficiency can be investigated in real time control. Genetic Algorithms are used to determine fuzzy membership functions. Experiment results show the better performances with FPSS in comparison to no PSS.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2251-2260
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• 2021

The motion control of the divertor maintenance system of the China Fusion Engineering Test Reactor (CFETR) was studied in this paper, in which CFETR Multi-Functional Maintenance Platform (MFMP) was simplified as a parallel robot for the convenience of theoretical analysis. In order to design the motion controller of parallel robot, the kinematics analysis of parallel robot was carried out. After that, the dynamic modeling of the hydraulic system was built. As the large variation of heavy payload on MFMP and highly nonlinearity of the system, A Fuzzy-PID controller was built for self-tuning PID controller parameters by using Fuzzy system to achieve better performance. In order to test the feasibility of the Fuzzy-PID controller, the simulation model of the system was built in Simulink. The results have showed that Fuzzy-PID controller can significantly reduce the angular error of the moving platform and provide the stable motion for transferring the divertor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1380-1384
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• 2015

The DC motor has the strong characteristics in the speed response, the system parameter variations and the external influence and is used as the speed controller with its good starting torque in the distributing industry. However development of the Microprocessor which is for high speed switching program can make better control system. This paper introduce to design of the high-effective DC motor controller that is using Software Bang-Bang Program of Fuzzy algorithm and to verify a PI controller and a Fuzzy controller.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.73-83
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• 1995

In this paper, we propose a generating method for the optimal rules of the fuzzy rule base using evolution algorithms. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and knowledge. can be intelligent control. The a, pp.oach presented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which is the defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method in non-linear systems.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• Journal of Power Electronics
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• v.14 no.3
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• pp.598-607
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• 2014

This paper proposes a frequency-locked loop (FLL) scheme for a single-phase grid-connected converter. A second-order generalized integrator (SOGI) based on fuzzy logic (FL) is applied to this converter to achieve precise phase angle detection. The use of this method enables the compensation of the nonlinear characteristic of the frequency error, which is defined in the SOGI scheme as the variation of the central frequency through the self-tuning gain. With the proposed scheme, the performance of the SOGI-FLL is further improved at the grid disturbances, which results in the stable operation of the grid converter under grid voltage sags or frequency variation. The PSIM simulation and experimental results are shown to verify the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1374-1376
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• 1996

In this paper We proposed a new method to generate fuzzy logic controllers through genetic algorithm(GA). In designing of fuzzy logic controllers encounters difficulties in the selection of optimized member-ship functions, gains and rule base, which is conventionally achieved by a tedious trial-and-error process. This paper develops genetic algorithms for automatic design of high performance fuzzy logic controllers which can overcome nonlinearities in many engineering control applications. The rule-base is coded in base-7 strings by generated from random function. Which can be presented in discrete fuzzy linguistic value, and using membership function with Gaussian curve. To verify the validity of this fuzzy logic controller it is compared with conventional fuzzy logic controller(FLC) and PID controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.50-59
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• 1998

This paper presents stabilization and adaptive control of flexible single link robot manipulator system by self-recurrent neural networks that is one of the neural networks and is effective in nonlinear control. The architecture of neural networks is a modified model of self-recurrent structure which has a hidden layer. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. When a flexible manipulator is rotated by a motor through the fixed end, transverse vibration may occur. The motor toroque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipuators so that it is arresed as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large changes in configuration common to robotic tasks requires dynamic models that describe both the rigid body motions, as well as the flexural vibrations. Therefore, a dynamic models for a flexible single link robot manipulator is derived, and then a comparative analysis was made with linear controller through an simulation and experiment. The results are proesented to illustrate thd advantages and imporved performance of the proposed adaptive control ove the conventional linear controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.371-376
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• 2003

In general, the electronic forklift driven by DC motor drive system is used in the industrial field. Classically, the DC motor is controlled by speed control using proportion control method, by output torque following the load on the plane like a manual operation. But in the industrial field, the electronic forklift is demanded the robust drive mode. Some cases of the mode, there are trouble in torque and speed control following slope capacity. The control is sensitive concerning with slope angle and output speed, various control method is studied for stability of speed control. We apply speed controller for the self-tuning using DSP(TMS320F240) as main controller for high speed processor, embody dynamic characteristic of control compared the PI control to the fuzzy control.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.69-77
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• 1999

This paper describes the design of fuzzy digital PID controller using simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous time linear digital PID controller. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy digital controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional digital PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.