• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.2
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1978-1987
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• 2014

Power requirement to the induction heating system varies during the heating process. A closed loop control is required to have a smooth control over the power. In this work, a constant frequency pulse density modulation based power tracking control scheme for domestic induction heating system is developed using the Fuzzy Logic Controller. In the conventional power modulation schemes, the switching losses increase with the change in the load. The proposed pulse density modulation scheme maintains minimum switching losses for the entire load range. This scheme is implemented for the class-D series resonant inverter system. Fuzzy logic controller based power tracking control scheme is developed for domestic induction heating power supply for various power settings. The open loop and closed loop simulation studies are done using the MATLAB/Simulink simulation tool. The control logic is implemented in hardware using the PIC16F877A microcontroller. Fuzzy controller tracks the set power by changing the pulse density of the gate pulses applied to the inverter. The results obtained are used to know the effectiveness of the fuzzy logic controller to achieve the set power.

• Journal of the Korean Society of Safety
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• v.14 no.1
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• pp.55-65
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• 1999

Fuzzy logic fuzzy set theory is recently getting increasing emphasis in process control applications. This paper describes application of fuzzy logic in a speed control system that uses a phase controlled bridge converter and a separately excited dc motor. The fuzzy control is used to linearize the transfer characteristics of the converter in discontinuous conduction mod occurring at light load and high speed. The fuzzy control is then extended to the current and speed control loops replacing the conventional PI control method. The control algorithms have been developed in detail and verified by simulation of a DC motor(DM) drive system. The simulation result indicates the superiority of fuzzy control over the conventional control methods. Fuzzy logic seems to have a lot of promise in the applications of power electronics.

• Journal of Biosystems Engineering
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• v.25 no.1
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• pp.63-70
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• 2000

In Korea, researches for year-round leaf vegetables production system are in progress and the most of them are focused on environment control. Automation technologies for harvesting , transporting and grading need to be developed. This study was conducted to develop harvesting process automation system profitable to a competitive price. 1. Manipulator and end-effector are to be designed and fabricated , and fuzzy logic controller for controlling these are to be composed. 2. The entire system constructed is to be evaluated through a performance test. A robot system for harvesting a lettuce was developed. It was composed of a manipulator with 20DOF (degrees of freedom) an end-effector, a lettuce feeding conveyor , an air blower , a machine vision device, 6 photoelectric sensors and a fuzzy logic controller. A fuzzy logic control was applied to determined appropriate grip force on lettuce. Leaf area index and height index were used as input parameters, and voltage was used as output parameter for the fuzzy logic controller . Success rate of the lettuce harvesting system was 93.06% , and average harvesting time was about 5 seconds per lettuce.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.574-578
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• 2001

The stability analysis for the fuzzy logic controller (FLC) has widely been reported. Furthermore many research in the FLC has been introduced to decrease the number of parameters representing the antecedent part of the fuzzy control rule. In this paper we briefly explain a single-input fuzzy logic controller (SFLC) or simple-structured FLC which uses only a single input variable. And then we analyze that it is absolutely stale based on the sector bounded condition. We also show the feasibility of the proposed stability analysis through a numerical example of a mass-damper-spring system.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.687-692
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• 1991

To reduce the complexity in constructing a fuzzy logic controller of multivariable systems, three major methods are presented. One is the method of constructing single-input-single-output fuzzy logic controllers after decoupling the target system. Another is the method of using fuzzy relation matrices which indicate the relation between each input and output. The other is the method of using the hierarchically classified inputs which dominantly influence one output than other inputs. Using the last two methods, simulation results of fuzzy logic controller implemented on 160MW boiler-turbine plant model are also shown.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.180-183
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• 1996

This work discusses simulation results for the fuzzy logic controller tested the project“Fuzzy Ramp Metering Algorithm Implementation.”The performance objectives were, in order of priority, to maximize total vehicle-miles, maximize mainline speeds, and minimize delay per vehicle while maintaining an acceptable ramp queue. In the fuzzy logic controller, the sensors from the on-ramps were helpful in maintaining reasonable ramp queue and mainline congestion because it considered these factors simultaneously. Each metered ramp had a parameter input file, which allowed the controller to be modified without recompiling the software. Consequently, maintenance costs should be minimal.

• Journal of the Korean Institute of Navigation
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• v.8 no.1
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• pp.15-42
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• 1984

Many studies have been done in the field of fuzzy logic theory, but it's application to the ship's steering system is few until this date. This paper is to survey the effect of application of fuzzy logic control by new compositional rule of Inference to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and output, and take the inputs derived from deviation angle and it's angular velocity. The Linguistic Control Rules are implemented on the digital computer to verify the performance of the fuzzy logic controller and simulations have been done in six cases of initial condition and disturbance type. Consequently, it was proved that the ship's steering system by introducing the F.L.C. is performed efficiently and less energy loss system compared with the conventional autopilot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.238-244
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• 2012

A modeling of vision based robot formation control system using fuzzy logic controller and extended Kalman filter is presented in this paper. The main problems affecting formation controls using fuzzy logic controller and vision based robots are: a robot's position in a formation need to be maintained, how to develop the membership function in order to obtain the optimal fuzzy system control that has the ability to do the formation control and the noise coming from camera process changes the position of references view. In order to handle these problems, we propose a fuzzy logic controller system equipped with a dynamic output membership function that controls the speed of the robot wheels to handle the maintenance position in formation. The output membership function changes over time based on changes in input at time t-1 to t. The noises appearing in image processing change the virtual target point positions are handled by Extended Kalman filter. The virtual target positions are established in order to define the formations. The virtual target point positions can be changed at any time in accordance with the desired formation. These algorithms have been validated through simulation. The simulations confirm that the follower robots reach their target point in a short time and are able to maintain their position in the formation although the noises change the target point positions.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.493-495
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• 1998

Robust design is one method to make manufacturing less sensitive to manufacturing process. Also it is cost effective technique to improve the quality process. This method uses statistically planned experiments to vary settings of important process control parameters. In this paper we apply fuzzy optimization and fuzzy logic system to robust design concept. First a method which uses fuzzy optimization in obtaining optimum settings by measured data from experiments will be presented. Second, fuzzy logic system is made to reduce experiments using experiments results consisted with key control parameter combinations. Then optimum parameter set points are obtained by the descrebed first fuzzy optimization method after prediction the results of each parameter combinations considering each control parameter variations by nonsingleton fuzzy logic system concept.