• 제목/요약/키워드: adaptive identification

검색결과 416건 처리시간 0.032초

적응 퍼지 궤환선형화기법을 이용한 유도전동기의 제어 (Control of induction motors using adaptive fuzzy feedback linearization techniques)

  • 류지수;김정중;이기상
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.1253-1256
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    • 1996
  • In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

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Stable discrete-time adaptive control for periodic systems

  • Ishitobi, Mitsuaki;Iwai, Zental
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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    • pp.717-720
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    • 1987
  • This paper presents a discrete-time model reference adaptive control technique for periodically time-varying plants. It is shown that the identification problem for periodic parameters can be reduced to that of constant unknown parameters case. The global stability of the resulting closed-loop system is established using the key technical lemma of Goodwin, Ramadge and Caines.

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동적 신경회로망을 이용한 비선형 시스템 제어 방식 (Control Method of Nonlinear System using Dynamical Neural Network)

  • 정경권;이정훈;김영렬;이용구;손동설;엄기환
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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    • pp.33-36
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    • 2002
  • In this paper, we propose a control method of an unknown nonlinear system using a dynamical neural network. The method proposed in this paper performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed algorithm, we simulated one-link manipulator. The simulation result showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

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메타놀자화균의 연속배양에 의한 균체생산의 온-라인 적응최적화 (Adaptive On-line Optimization of Cellular Productivity of Continuous Methylotroph Culture)

  • 이형춘;박정오
    • 한국식품영양학회지
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    • 제1권2호
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    • pp.31-36
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    • 1988
  • An adaptive on-line optimization method has been applied to test the ability to maximize the cellular productivity of a continuous methylotroph culture system which was simulated by a variable yield Monod-type model. Optimum dilution rate and productivity were successively obtained and maintained at all times by the algorithm that utilizes steepest descent technique as optimization method and recursive least-square method with forgetting factor as dynamic model identification.

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다중모델과 스위칭을 이용한 로봇 매니퓰레이터의 적응제어 (Adaptive Control of Robotic Manipulators Using Multiple Models and)

  • 이형찬
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1997년도 하계학술대회 논문집 B
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    • pp.693-695
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    • 1997
  • This paper deals with the tracking control problem of robotic manipulators with unknown or changing dynamics. The torque input applied to the joint actuators is determined at every instance by the identification model that best approximates the robot dynamics. The best of the identified model is chosen by the proposed switching mechanism with fuzzy inference of the manipulator in an indirect adaptive controller architecture. Simulation results are also included to demonstrate the improvement in the tracking performance when the proposed method is used.

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지능형 자동차를 위한 적응 주행제어 및 감시시스템에 관한 연구 (A Study on Adaptive Cruise Control and Monitoring System for Intelligent Vehicle)

  • 양승현;이석원
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2006년도 하계종합학술대회
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    • pp.909-910
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    • 2006
  • In this paper, the transfer function to the vehicle is derived from using system identification algorithm in connection with the driving vehicle. We design the adaptive cruise controller using the derived transfer function, and make it possible to monitoring and control the vehicle in real time using embedded system and technology of Internet.

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신경회로망을 이용한 비선형 동적인 시스템의 효과적인 인식모델에 관한 연구 (The Study on the Indirect Adaptive Control of Nonlinear System using Neural Network)

  • 김성주;이상배
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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    • pp.249-257
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    • 1995
  • In this paper, we demeonstrate that neural networks can be used effectively for the control of nonlinear dynamical system. To adaptively control a plant, there are two distinct approach. these are direct control and indirect control. Both direct and Indirect adaptive control are trained using static back propagation. In indirect, using the resulting identification model, which contains neural networks and linear dynamical elements as subsystems, the parameters of the controller are adjusted.

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Adaptive Slot-Count Selection Algorithm based on Tag Replies in EPCglobal Gen-2 RFID System

  • 임인택
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 2011년도 추계학술대회
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    • pp.653-655
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    • 2011
  • EPCglobal proposed a Q-algorithm, which is used for selecting a slot-count in the next query round. However, it is impossible to allocate an optimized slot-count because the original Q-algorithm did not define an optimized weight C value. In this paper, we propose an adaptive Q-algorithm, in which we differentiate the weight values with respect to collision and empty slots. The weight values are defined with the identification time as well as the collision probability.

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적분 동작이 포함된 적응제어기 (Adaptive Controllers with Integral Action)

  • 한홍석;양해원
    • 대한전기학회논문지
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    • 제37권4호
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    • pp.220-225
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    • 1988
  • A class of adaptive controllers with integral action is proposed, which may riject the offset due to any load disturbance on the plant. Effective integral action and robust identification against the offset can be achieved via the zero-gain predictor. The system is improved, in this paper, to be of more generalized structure, and the detuning control weight which can cope with nonminimum-phase systems is tuned on-line. Discrete-time versions of the improved system are developed, which may be more flexible for the choice of the design parameters. The resulting control systems may also be shown to be robust to the unmodelled dynamics.

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덕트에서 온라인 적응 알고리듬을 이용한 능동소음제어 (Active noise control with on-line adaptive algorithm in a duct system)

  • 김흥섭;홍진석;오재응
    • 대한기계학회논문집A
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    • 제21권8호
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    • pp.1332-1338
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    • 1997
  • In the case of the transfer function for the secondary path is dependent on time, the on-line method which can model it is continuously must be applied to the active noise control technique. And the adaptive random noise technique among the on-line methods is effective in the narrow-band control. In this method, the signal to noise ratio between random noise for modeling and primary noise is low. Therefore, the estimations of transfer function will be prone to inaccuracies and the convergence time will be too long. Such imperfections will have an influence upon the performance of an active noise controller. In this study, t enhance the signal to noise ratio, the on-line method that is combined the conventional adaptive random noise technique and the adaptive line enhancer, is proposed. By using proposed on-line method, a rigorous system identification and control of primary noise have been implemented.