• 제목/요약/키워드: Neuro-Controller

검색결과 221건 처리시간 0.025초

뉴로-퍼지 제어기를 이용한 전력시스템의 발전량 증가율 제한에 관한 연구 (A study on Generation rate Constraints of Power System using Neuro-Fuzzy Controller)

  • 김상효;이창우;주석민;정동일;정형환
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2002년도 하계학술대회 논문집 A
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    • pp.301-303
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    • 2002
  • The load frequency control of power system is one of important subjects in view of system operation and control. To converge within allowance load variation value the frequency and tie-line power flow deviation of each areas, we should regulate the active power output of power plant for regulation in system Applying the NFC(Neuro-Fuzzy Controller) to the model of load frequency control of 2-area power system, we prove that the control is superior to the conventional control technique through computer simulation. For verification of robustness, when we consider generator-rate constraint similar to nonlinearities of power system.

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뉴로-퍼지 네트워크에 의한 유도전동기 궤적의 학습에 관한 연구 (A Study on the Learning Method for Induction Motor Trajectory using a Neuro-Fuzzy Networks)

  • 양승호;김세찬;김덕헌;유동욱;원충연
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1994년도 하계학술대회 논문집 A
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    • pp.331-333
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    • 1994
  • A learning method for induction motor trajectory using neuro-fuzzy networks (NFN) based on fusion of fuzzy logic theory and neural networks is proposed. The premise and consequent parameters of the NFN affecting the controllers performances are modified during the learning stages by the proposed learning method to implement an optimal controller only with pre-determined target trajectory and the least amount of knowledge about an induction motor. The induction motor position control system is simulated to verify the effectiveness of the learned NF controller(NFC). The simulation results shows that the proposed learning method has good dynamic performance and small steady state error.

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Vibration control of 3D irregular buildings by using developed neuro-controller strategy

  • Bigdeli, Yasser;Kim, Dookie;Chang, Seongkyu
    • Structural Engineering and Mechanics
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    • 제49권6호
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    • pp.687-703
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    • 2014
  • This paper develops a new nonlinear model for active control of three-dimensional (3D) irregular building structures. Both geometrical and material nonlinearities with a neuro-controller training algorithm are applied to a multi-degree-of-freedom 3D system. Two dynamic assembling motions are considered simultaneously in the control model such as coupling between torsional and lateral responses of the structure and interaction between the structural system and the actuators. The proposed control system and training algorithm of the structural system are evaluated by simulating the responses of the structure under the El-Centro 1940 earthquake excitation. In the numerical example, the 3D three-story structure with linear and nonlinear stiffness is controlled by a trained neural network. The actuator dynamics, control time delay and incident angle of earthquake are also considered in the simulation. Results show that the proposed control algorithm for 3D buildings is effective in structural control.

신경 회로망을 이용한 비최소 위상 시스템의 최적 제어기 설계 (Design of an Optimal Controller with Neural Networks for Nonminimum Phase Systems)

  • 박상봉;박철훈
    • 전자공학회논문지C
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    • 제35C권6호
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    • pp.56-66
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    • 1998
  • 본 논문은 비최소 위상 시스템을 보다 효율적으로 제어하기 위하여 기존의 PID 타입의 선형 제어기와 병렬적으로 구성된 신경망 제어기의 구성에 대하여 다룬다. 제안된 제어기의 제어 목표는 비최소 위상 시스템의 제어의 경우 흔히 나타나는 언더슛 현상을 최소화하면서 설정된 시스템 응답과의 응답 오차가 최소화하는 것이다. 전체 비용 함수는 고려된 두 가지의 개별 목적 함수간의 선형 합으로 이루어 진다. 신경망 제어기는 주어진 전체 제어 시간 동안의 제어 성능을 광역 평가를 통하여 주어진 전체 비용 함수를 최소화하는 최적 제어기를 구성하도록 진화 프로그래밍을 이용하여 off-line으로 학습된다. 일반적인 컴퓨터 모의 실험으로 계단 신호 응답에서 나타나는 빠른 settling 시간, 작은 언더슛과 오버슛과 같은 제어 성능 향상의 관점에서 기존의 선형 제어 시스템의 성능에 비해 훨씬 효과적이라는 것을 보인다. 또한, 파렛토(pareto) 다중 최적화 개념을 도입하여 선형 합으로 이루어진 비용 함수 최적화의 한계성과 문제점을 극복한다.

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자기학습 퍼지제어기를 사용한 하이브리드 제어기 설계 (A Design of Hybrid Controller Using Self-Learning Fuzzy Controller)

  • 양혜원;이호형
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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    • pp.207-209
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    • 1995
  • The PID controller is widely used due to its fast response and robustness. But its performance is not so good compared with modem controllers such as adaptive, robust, fuzzy, neural controller. Therefore, it is natural to replace PID controller by modem controllers. But, the problem is that modem controller can not be easily applied to the real time process. Hence, this paper proposes such a structure that PID controller and Self-Learning Fuzzy Controller(SLFC) are in parallel with each other. The parameter of SLFC will be updated by gradient descent method using neuro - identifier. The usefulness of this hybrid controller will be proved by simulation results.

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Intelligent Washing Machine: A Bioinspired and Multi-objective Approach

  • Milasi, Rasoul Mohammadi;Jamali, Mohammad Reza;Lucas, Caro
    • International Journal of Control, Automation, and Systems
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    • 제5권4호
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    • pp.436-443
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    • 2007
  • In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

NFC와 ANN을 이용한 IPMSM 드라이브의 속도 추정 및 제어 (Speed Estimation and Control of IPMSM Drive using NFC and ANN)

  • 이정철;이홍균;정동화
    • 전력전자학회논문지
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    • 제10권3호
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    • pp.282-289
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    • 2005
  • 본 논문에서는 NFC(Neuro-Fuzzy Controller)와 ANN(Artificial Neural network) 제어기를 이용한 IPMSM의 속도 제어 및 추정을 제시한다. PI 제어기에서 나타나는 문제점을 해결하기 위하여 신경회로망과 퍼지제어를 혼합적용한 NFC를 설계한다. 신경회로망의 고도의 적응제어와 퍼지 제어기의 강인성 제어의 장점들을 접목한다. 다음은 ANN을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 2층 구조를 가진 신경회로망에 BPA(Back Propagation Algorithm)를 적용하여 IPMSM 드라이브의 속도를 추정한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다.

무선 매체 접근 제어 프로토콜 상에서의 음성/데이타 통합 시스템을 위한 뉴로 퍼지 제어기 설계 (Design of a NeuroFuzzy Controller for the Integrated System of Voice and Data Over Wireless Medium Access Control Protocol)

  • 최원석;김응주;김범수;임묘택
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 D
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    • pp.1990-1992
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    • 2001
  • In this paper, a NeuroFuzzy controller (NFC) with enhanced packet reservation multiple access (PRMA) protocol for QoS-guaranteed multimedia communication systems is proposed. The enhanced PRMA protocol adopts mini-slot technique for reducing contention cost, and these minislot are futher partitioned into multiple MAC regions for access requests coming from users with their respective QoS (quality-of-service) requirements. And NFC is designed to properly determine the MAC regions and access probability for enhancing the PRMA efficiency under QoS constraint. It mainly contains voice traffic estimator including the slot information estimator with recurrent neural networks (RNNs) using real-time recurrent learning (RTRL), and fuzzy logic controller with Mandani- and Sugeno-type of fuzzy rules. Simulation results show that the enhanced PRMA protocol with NFC can guarantee QoS requirements for all traffic loads and further achieves higher system utilization and less non real-time packet delay, compared to previously studied PRMA, IPRMA, SIR, HAR, and F2RAC.

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뉴로 퍼지 시스템을 이용한 비선형 시스템의 IMC 제어기 설계 (Design of IMC for Nonlinear Systems by Using Adaptive Neuro-Fuzzy Inference System)

  • 김성호;강정규
    • 제어로봇시스템학회논문지
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    • 제7권11호
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    • pp.958-961
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    • 2001
  • Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

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Application of neuro-fuzzy algorithm to portable dynamic positioning control system for ships

  • Fang, Ming-Chung;Lee, Zi-Yi
    • International Journal of Naval Architecture and Ocean Engineering
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    • 제8권1호
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    • pp.38-52
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    • 2016
  • This paper describes the nonlinear dynamic motion behavior of a ship equipped with a portable dynamic positioning (DP) control system, under external forces. The waves, current, wind, and drifting forces were considered in the calculations. A self-tuning controller based on a neuro-fuzzy algorithm was used to control the rotation speed of the outboard thrusters for the optimal adjustment of the ship position and heading and for path tracking. Time-domain simulations for ship motion with six degrees of freedom with the DP system were performed using the fourth-order RungeeKutta method. The results showed that the path and heading deviations were within acceptable ranges for the control method used. The portable DP system is a practical alternative for ships lacking professional DP facilities.