• 제목/요약/키워드: Fuzzy control

검색결과 4,185건 처리시간 0.037초

Optimal Control of Induction Motor Using Immune Algorithm Based Fuzzy Neural Network

  • Kim, Dong-Hwa;Cho, Jae-Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.1296-1301
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    • 2004
  • Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

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적응 퍼지-뉴로 제어기의 설계와 응용 (Design & application of adaptive fuzzy-neuro controllers)

  • 강경운;김용민;강훈;전홍태
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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    • pp.710-717
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    • 1993
  • In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

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Fuzzy H$\infty$ Filtering for Nonlinear Systems with Time-Varying Delayed States

  • Lee, Kap-Rai;Lee, Jang-Sik;Oh, Do-Chang;Park, Hong-Bae
    • Transactions on Control, Automation and Systems Engineering
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    • 제1권2호
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    • pp.99-105
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    • 1999
  • This paper presents a fuzzy H$\infty$ filtering problem for a class of uncertain nonlinear systems with time-varying delayed states and unknown inital state on the basis of Takagi-Sugeno(T-S) fuzzy model. The nonlinear systems are represented by T-S fuzzy models, and the fuzzy control systems utilize the concept of the so-called parallel distributed compensation. Using a single quadraic Lyapunov function, the stability and L2 gain performance from the noise signals to the estimation error are discussed. Sufficient conditions for the existence of fuzzy H$\infty$ filters are given in terms of linear matrix inequalities (LMIs). The filtering gains can also be directly obtained from the solutions of LMIs.

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Real-Time Digital Fuzzy Control Systems considering Computing Time-Delay

  • Park, Chang-Woo;Shin, Hyun-Seok;Park, Mig-Non
    • 한국지능시스템학회논문지
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    • 제10권5호
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    • pp.423-431
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    • 2000
  • In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.

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영상검지기를 이용한 실시간 교통신호 감응제어 (A Development of a Real-time, Traffic Adaptive Control Scheme Through VIDs.)

  • 김성호
    • 대한교통학회지
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    • 제14권2호
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    • pp.89-118
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    • 1996
  • The development and implementation of a real-time, traffic adaptive control scheme based on fuzzy logic through Video Image Detector systems (VIDs) is presented. Through VIDs based image processing, fuzzy logic can be used for a real-time traffic adaptive signal control scheme. Fuzzy control logic allows linguistic and inexact traffic data to be manipulated as a useful tool in designing signal timing plans. The fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategy based on a priori verbal communication. The implementation of fuzzy logic controller for a traffic network is introduced. Comparisons are made between implementations of the fuzzy logic controller and the actuated controller in an isolated intersection. The results obtained from the application of the fuzzy logic controller are also compared with those corresponding to a pretimed controller for the coordinated intersections. Simulation results from the comparisons indicate the performance of the system is between under the fuzzy logic controller. Integration of the aforementioned schemes into and ATMS framework will lead to real-time adjustment of the traffic control signals, resulting in significant reduction in traffic congestion.

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퍼지 PID 제어를 이용한 추종 제어기 설계 (Design of Fuzzy PID Controller for Tracking Control)

  • 김봉주;정정주
    • 제어로봇시스템학회논문지
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    • 제7권7호
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    • pp.622-631
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    • 2001
  • This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

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Optimal Learning of Fuzzy Neural Network Using Particle Swarm Optimization Algorithm

  • Kim, Dong-Hwa;Cho, Jae-Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.421-426
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    • 2005
  • Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

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복합자석형 자기부상차량의 PID제어와 Fuzzy제어 (PID control and fuzzy control of hybrid magnetic levitation system)

  • 권병일
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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    • pp.699-703
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    • 1991
  • A magnetic levitation system with hybrid magnets, which is composed of permanent magnets and electromagnets, consumes less power than the conventional attraction type system. In this paper, we propose PID controller and PID-Fuzzy controller for hybrid magnet. We first present "constant gap" control technology with PID controller. Secondly, "zero power" control technology with PID-Fuzzy hybrid controller is presented.roller is presented.

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이동로봇의 행동제어를 위한 2-Layer Fuzzy Controller (2-Layer Fuzzy Controller for Behavior Control of Mobile Robot)

  • 심귀보;변광섭;박창현
    • 한국지능시스템학회논문지
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    • 제13권3호
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    • pp.287-292
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    • 2003
  • 로봇의 기능이 다양해지며 복잡해지고 있다. 주위의 환경을 감지하는 센서로는 거리정보 뿐만 아니라 영상 정보, 음성 정보까지 이용하고 있다. 본 논문에서는 다양한 입력정보를 가진 로봇을 제어하기 위한 알고리즘으로 2-layer fuzzy control을 제안한다. 장애물 회피의 경우에 다수의 거리 센서를 이용하는데 이것을 앞쪽, 왼쪽, 오른쪽으로 분류하여 3개의 sub-controller를 가지고 퍼지 추론을 한 다음, 2단계에서는 이 3개의 sub-controller의 출력으로 조합된 퍼지 추론을 하여 통합적인 제어를 한다. 본문에서는 2-layer fuzzy controller와 비슷한 구조를 갖는 hierarchical fuzzy controller와 비교를 하였으며 robot following에도 적용하여 각각에 대한 시뮬레이션과 실험을 통해 성능을 확인한다.

자기조정 퍼지 PID제어기를 이용한 전력시스템의 부하주파수 제어 (Load Frequency Control of Power System using a Self-tuning Fuzzy PID Controller)

  • 이준탁
    • Journal of Advanced Marine Engineering and Technology
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    • 제23권1호
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    • pp.40-46
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    • 1999
  • A self-tuning FPID(Fuzzy Proportional Intergral Derivative) controller fo load frequency control of 2-area power systemis proposed in this paper. The paramters of the proposed self-tuning FPID controller are self-tuned by the proposed fuzzy inference technique. Therefore in this paper the fuzzy inference technique of PID gains using PSGM(Product Sum Gravity Method) is presented and is applied to the load frequency control of 2-area power system. The computer simulation results show that the proposed controller give better more control characteristics than convention-al PID, FLC under load changes.

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