• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1235-1244
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• 2017

This paper proposes a fuzzy logic controlled new topology of high voltage gain zero voltage switching (ZVS) asymmetrical PWM full-bridge DC-DC boost converter for constant load and high power applications. The APWM full-bridge stage provides high voltage gain and soft-switching characteristics increase the efficiency and reduce the switching losses. Fuzzy logic controller (FLC) improves the performance and dynamic characteristics of the proposed converter. A comparison with a classical proportional-integral (PI) controller demonstrates the high performances of the proposed technique in terms of effective output voltage regulation under different operating conditions. Simulation is done by integrating two different simulation platforms $PSIM^{(R)}$ and $Matlab^{(R)}/Simulink^{(R)}$ by using SimCoupler tool of $PSIM^{(R)}$. Experimental results using 120W load have been provided to validate the results.

• Journal of Biosystems Engineering
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• v.22 no.4
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• pp.487-496
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• 1997

A fuzzy logic controller(FLC) was developed for the autonomous travel of speedsprayer in an orchard. The autonomous travel with the FLC was graphically simulated under the conditions of an ordinary standard orchard. Differential global positioning system(DGPS) was used to find the direction of running and four ultrasonic sensors were used to detect obstacles during the running. The simulation results showed that the speedsprayer, by the FLC combined with DGPS and the ultrasonic sensors. could overcome the turning problem at comers which could not be solved with such a system as machine vision and might be operated autonomously.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.993-996
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• 1993

This paper addresses the structure and its associated learning algorithms of a feedforward multi-layered connectionist network, which has distributed learning abilities, for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed neural-network-based fuzzy logic control system (NN-FLCS) can be contrasted with the traditional fuzzy logic control system in their network structure and learning ability. An on-line supervised structure/parameter learning algorithm dynamic learning algorithm can find proper fuzzy logic rules, membership functions, and the size of output fuzzy partitions simultaneously. Next, a Reinforcement Neural-Network-Based Fuzzy Logic Control System (RNN-FLCS) is proposed which consists of two closely integrated Neural-Network-Based Fuzzy Logic Controllers (NN-FLCS) for solving various reinforcement learning problems in fuzzy logic systems. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. As ociated with the proposed RNN-FLCS is the reinforcement structure/parameter learning algorithm which dynamically determines the proper network size, connections, and parameters of the RNN-FLCS through an external reinforcement signal. Furthermore, learning can proceed even in the period without any external reinforcement feedback.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1034-1036
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• 1996

In this paper, an adaptive fuzzy logic controller is presented for auto-tunning of the scaling factors by using learning capability of neural networks. The proposed scheme consists of the FLC which includes the PI-type FLC and PD-type FLC in parallel form and the neural network which learns scale factors of FLC. Computer simulations were performed to illustrate the effectiveness of a proposed scheme. A proposed FLC controller was applied to the second order system and velocity control of the brushless DC motors. For the design of the FLC, tracking error, change of error, and acceleration error are selected as input variables of the FLC and three seal e factors were used in the parallel-type FLC. This scheme can be used to reduce the difficulty in the selection of the scale factors.

• Journal of Engineering Education Research
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• v.3 no.1
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• pp.21-26
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• 2000

The World Wide Web provides new opportunities for cyber education over the Internet. The web, when combined with other network tools, can be used to provide useful educational information to learners. Thus, the objective of this paper is to develop Java applet for understanding the concept of Fuzzy Logic Controller (FLC) on the Internet. The developed Java Applet is composed of four frames: fuzzifier, rulebase, inference engine and defuzzifier. Since data transmission can be achieved from one frame to other frames, users can easily observe and understand the process of FLC. The results of this paper can be used to improve the efficiency of lectures in the cyber university.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.225-228
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• 2002

When the fuzzy logic controller (FLC), which is designed based on the plant model, is applied to the real control system, satisfactory control performance may not be attained due to modeling errors from the plant model. In such cases, the control parameters of the controller must be adjusted to enhance control performance. Until now, the trial and error method has been used, consuming much time and effort. To resolve such problem, response surface methodology (RSM), a new method of adjusting the control parameters of the controller, is suggested. This method is more systematic than the previous trial and error method, and thus optimal solutions can be provided with less tuning. First, the initial values of the control parameters were determined through the plant model and the optimization algorithm. Then, designed experiments were performed in the region around the initial values, determining the optimal values of the control parameters which satisfy both the rise time and overshoot simultaneously.

• 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 Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.233-237
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• 2012

In this paper, We propose a control system which compensate a output of a main Neual Network using a RTDNN(Recurrent Time Delayed Neural Network) with a FLC(Fuzzy Logic Controller)After a learn of main neural network, it can occur a Over shoot or Under shoot from a disturbance or a load variations. In order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. We can confirm good response characteristics of proposed neural network controller by the results of simulation.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.111-118
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• 1995

This paper presents the modified fuzzy-compensated PID(FCPID) control, which involves adding the compensator to an existing PID controller, to improve the performances of the systems. Compared to a conventional PID control and a fuzzy logic control(FLC), the proposed control scheme has superior performance. Experimental results of an actual implementation of the modified PC-based FCPID controller on the DC servo-motor demonstrate considerable improve- ment of the performance of the existing FCPID control by monitoring the scaling factor. They show faster responses and smaller overshoots than the conventional FCPID control scheme for the various reference inputs and the robustness to the loads.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.620-622
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• 2004

Mobility of an indoor wheeled robot is affected by adhesion force that is related to various floor conditions. When the adhesion force between driving wheels and the floor decreases suddenly, the robot has a slip state. In order to overcome this slip problem, optimal slip velocity must be decided for stable movement of wheeled robot. First of all, this paper shows that conventional PI control can not be applied to a wheeled robot of the light weigh. Secondly, reposed fuzzy logic applied by the Takagi-Sugeno model for the configuration of fuzzy sets. For the design of Takaki-Sugeno model and fuzzy rule, proposed algorithm uses FCM(Fuzzy c-mean clustering method) algorithm. In additionally, this algorithm controls recovered driving torque for the restrain the re-slip. The proposed fuzzy logic controller(FLC) is pretty useful with prevention of the slip phenomena through that compare fuzzy with PI control for the controller performance in the re-adhesion control strategy. These procedures are implemented using a Pioneer 2-DXE wheeled robot parameter.