• Journal of Communications and Networks
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• 제7권3호
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• pp.337-352
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• 2005

In this paper, a fuzzy logic implementation of the random early detection (RED) mechanism [1] is presented. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Based on previous studies, it is clear that the performance of RED algorithm is extremely related to the traffic load as well as to its parameters setting. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of the RED gateway. To achieve this goal, a two-input-single-output fuzzy controller is used. To achieve a low packet loss probability, the proposed fuzzy controller is responsible to control the $max_{p}$ parameter of the RED gateway. The inputs of the proposed fuzzy controller are 1) the difference between average queue size and a target point, and 2) the difference between the estimated value of incoming data rate and the target link capacity. To evaluate the performance of the proposed fuzzy mechanism, several trials with file transfer protocol (FTP) and burst traffic were performed. In this study, the ns-2 simulator [2] has been used to generate the experimental data. All simulation results indicate that the proposed fuzzy mechanism out performs remarkably both the traditional RED and Adaptive RED (ARED) mechanisms [3]-[5].

• 대한기계학회논문집
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• 제17권7호
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• pp.1813-1822
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• 1993

An algorithm of fuzzy variable structrue control is proposed to design a closed loop fuel-injection system for the emission control of automotive gasoline engines. Fuzzy control is combined with sliding control at the switching boundary layer to improve the chattering of the stoichiometric air to fuel ratio. Multi-staged fuzzy rules are introduced to improve the adaptiveness of control system for the various operating conditions of engines, and a simplified technique of fuzzy inference is also adopted to improve the computational efficiency based on nonfuzzy micro-processors. The proposed method provides an effective way of engine controller design due to its hybrid structure satisfying the requirements of robustness and stability. The great potential of the fuzzy variable structure control is shown through a hardware-testing with an Intel 80C186 processor for controller and a typical engine-only model on an AD-100 computer.

• 전자공학회논문지B
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• 제33B권3호
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• pp.17-27
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• 1996

More fuzzy hardware are expected to be utilized in the future to construct fuzzy logic controllers (FLCs). It is hard to find an existing fuzzy hardware which is adopting advanced functions such as self-tuning algorithm in addition to the conventional inference calculation. That is mainly because conventional self-tuning algorithms designed to implement with some hardware circuits is required for fuzzy hardwares to have self-tuning capability. As a first step toward the feature, a novel self-tuning algorithm is proposed in this paper. Based on the search method, the main idea of the proposed algorithm is to detemine valid ranges of input variables of an FLC in order to maximize performance indices fo the control system. The performance indices are so ismple as to be realized by hardware circuit. in dadditon to the conventional scaling-factor adjustment, the algorithm adjusts offset values as well, which, in effect, modifies fuzzy rules of the FLC. To justify the performance of the proposed algorithm, a simulation study is executed.

• 전자공학회논문지B
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• 제32B권8호
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Journal of Advanced Marine Engineering and Technology
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• 제24권6호
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• pp.53-60
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• 2000

In this paper, we design a GA-fuzzy controller for position control and anti-swing at the destination point. A genetic algorithm is used to complement the demerits such as the difficulty of the component selection of the fuzzy controller, namely, scaling factors, membership functions and control rules. Lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling. Simulation results show that the proposed control technique is superior to a conventional optimal control in destination point moving and modification.

• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.597-604
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• 2001

Recently, the study of the moving sliding mode in the variable structure control is in progress ac-tively. The conventional time-invariant sliding model control can\`t guarantee the sliding mode in the reaching phase, which is robust against the uncertainty. But with the time-varying method, the controller makes the states track the desired trajectories and keeps the sliding mode. Nevertheless, the piecewise continuous method of the past still has the reaching mode. Thus we propose the continuously moving sliding surface by the fuzzy algorithm. The proposed algorithm is made of the fuzzy rule considering both the error and the error velocity, and may apply to the entire phase plane without sacrificing sliding mode. Especially the proposed scheme can rotate tot he slope-decreasing direction, needless to say rotating to the slope-increasing direction. For showing that the proposed controller guarantees the sliding model and ensures the robustness, we apply the proposed method to the two-link robot manipulator simulation.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.821-825
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• 2008

본 논문에서는 전기로에 투입되는 합금철의 중량을 보다 정밀하게 제어하기 위하여 Mamdani 타입의 퍼지 알고리듬을 적용하였다. 고안된 퍼지 시스템은 중량 오차 및 변화를 입력으로 하고 합금철 투입 바이브레이터의 진동의 크기를 출력으로 하며, programmable logic controller의 래더 프로그램으로 구현하여 현장에 적용하였다. 현장에 적용된 퍼지 제어 알고리듬은 기존의 온-오프 제어기에 비하여 전기로에 투입되는 합금철의 계량 정밀도를 높였을 뿐 아니라, 합금철 투입 시간도 크게 단축시켰다.

• 전기학회논문지
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• 제63권10호
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• pp.1384-1392
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• 2014

This paper presents a torque ripple reduction algorithm for the switched reluctance motor drives using the fuzzy logic and the sliding mode control. A turn-on angle controller based on the fuzzy logic determines the optimal turn-on angle. In addition, a sliding mode torque control (SMTC) methods reduces torque ripples instantaneously in the commutation region. The proposed algorithm does not require complex system models considering nonlinear magnetizing or demagnetizing periods of the phase current. According to the rotor speed and torque, the proposed controller changes the turn-on angle and reference torque instantaneously until the torque ripples are minimized. The simulation and experimental results verify the validity of minimizing the torque ripple performance.

• 지능정보연구
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• 제8권1호
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• pp.87-101
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• 2002

본 논문에서는 컬러 정보와 퍼지 c-means 알고리즘을 이용한 차량 번호판 인식 방법을 제안하고 차량 번호판 인식을 이용한 주차관리시스템 개발에 대해서 기술한다 컬러 정보와 퍼지 c-means알고리즘을 이용한 차량 번호판 인식 기술은 차량의 영상에서 번호판을 추출하는 부분과 추출한 번호판 영역에서 문자를 인식하는 부분으로 구성된다 본 논문에서는 최빈수 평활화를 이용하여 차량 영상에서 녹색 잡음을 제거하고 RGB컬러에서 녹색 정보와 횐색 정보를 이용하여 번호판 영역을 추출하였다. 추출된 번호판 영역의 코드들은 히스토그램 방법을 이용하여 추출하였고 FCM(Fuzzy c-means) 알고리즘을 이용하여 차량 번호판을 인식하였다. 80개의 실제 차량 영상을 대상으로 실험한 결과는 제안된 번호판 영역 추출 방법이 기존의 RGB정보를 이용한 방법과 HSI를 이용한 방법보다 추출율이 개선되었다 그리고 FCM 알고리즘을 이용한 차량 번호판 인식이 효율적인 것을 확인하였다. 실험을 통하여 성능 향상을 보인 제안된 차량 번호판 방법을 이용하여 주차관리시스템을 개발하였다.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.448-457
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• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.