• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.46-54
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• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.2
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• pp.35-40
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• 1994

Fuzzy logic controllers are often found to behave better than PI controllers. One of the major reasons for this is that the fuzzy logic inferences used can produce nonlinear type controllers. For some applicatioins, howeveer, linear fuzzy logic controllers also perofrm better than PI controllers. In this paper, we examine linear fuzzy logic controllers to show that the truncation effects of the fuzzy logic controllers make them perform much better than the PI controllers. In terms of a performance index we used, the truncation effects reduced the index value by up to 80% for examples we studied.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.781-784
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• 1993

Most linguistic models of processes or plants known are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show two models for synchronous finite state machines (FSM) based on fuzzy logic, namely the Crisp-State-Fuzzy-Output (CSFO FSM) and Fuzzy-State-Fuzzy Output (FSFO FSM). As a result of the introduction of the FSM models, the improved architectures for fuzzy logic controller have been defined. These architectures featuring pipelined intelligent fuzzy controller are discussed in terms of dimensionality of the model. VLSI integrated circuit implementation issues of the fuzzy logic controller are also considered. The presented approach can be utilized for fuzzy controller hardware accelerators intended to work in the real-time environment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1468-1474
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• 2001

In this paper, a new fuzzy-logic anti-swing control scheme is proposed for a three-dimensional overhead crane. The proposed control consists of a position servo control and a fuzzy-logic control. The position servo control is used to control the trolley position and rope length, and the fuzzy-logic control is used to suppress load swing. The proposed control guarantees not only prompt suppression of load swing but also accurate control of trolley position and rope length for the simultaneous travel, traverse, and hoisting motions of the crane. The effectiveness of the proposed control is shown by experiments with a prototype three-dimensional overhead crane.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.7-12
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• 2005

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2258-2263
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• 2003

This paper presents the design and development of an optimal fuzzy logic controller (FLC) for a laser tracking system. An optimal intelligent fuzzy logic controller was founded on integral criterion of the fuzzy models and three-dimensional fuzzy control. Research had been also concentrated on the methods for multivariable fuzzy models for the purposes of real-time process. Simulation results have shown remarkable tracking performance of this fuzzy PID controller.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.491-494
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• 1997

In this paper, we suppose a fuzzy logic controller for cruise control of vehicle. Generally, fuzzy logic controller is known as a controller which can be coped with a non-linear and a complex system. The proposed fuzzy logic controller consists of three input variables; that is, a desired speed, a current vehicle speed, and a current acceleration, and one output variable, throttle angle. The supposed fuzzy logic controller is for engine speed control system is implemented on 80586 microprocessor with DT-2801.

• 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.

• Structural Engineering and Mechanics
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• v.27 no.1
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• pp.77-97
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• 2007

When assessing buildings that may collapse during a large earthquake, conventional rapid visual screening procedures generally provide good results when identifying buildings for further investigation. Unfortunately, their accuracy at identify buildings at risk is not so good. In addition, there appears to be little room for improvement. This paper investigates an alternative screening procedure based on fuzzy logic and artificial neural networks. Two databases of buildings damaged during the Athens earthquake of 1999 are used for training purposes. Extremely good results are obtained from one database and not so good results are obtained from the second database. This finding illustrates the importance of specifically collecting data tailored to the requirements of the fuzzy logic based rapid visual screening procedure. In general, results demonstrate that the trained fuzzy logic based rapid visual screening procedure represents a marked improvement when identifying buildings at risk. In particular, when smaller percentages of the buildings with high damage scores are extracted for further investigation, the proposed fuzzy screening procedure becomes more efficient. This paper shows that the proposed procedure has a significant optimisation potential, is worth pursuing and, to this end, a strategy that outlines the future development of the fuzzy logic based rapid visual screening procedure is proposed.

• 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.