• 제목/요약/키워드: Adaptive learning control

검색결과 361건 처리시간 0.035초

실시간 적응 학습 제어를 위한 진화연산(II) (Evolutionary Computation for the Real-Time Adaptive Learning Control(II))

  • 장성욱;이진걸
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2001년도 춘계학술대회논문집B
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    • pp.730-734
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    • 2001
  • In this study in order to confirm the algorithms that are suggested from paper (I) as the experimental result, as the applied results of the hydraulic servo system are very strong a non-linearity of the fluid in the computer simulation, the real-time adaptive learning control algorithms is validated. The evolutionary strategy has characteristics that are automatically. adjusted in search regions with natural competition among many individuals. The error that is generated from the dynamic system is applied to the mutation equation. Competitive individuals are reduced with automatic adjustments of the search region in accord with the error. In this paper, the individual parents and offspring can be reduced in order to apply evolutionary algorithms in real-time as the description of the paper (I). The possibility of a new approaching algorithm that is suggested from the computer simulation of the paper (I) would be proved as the verification of a real-time test and the consideration its influence from the actual experiment.

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디지털 시그널 프로세서를 이용한 로봇 매니퓰레이터의 적응-신경제어 (The Adaptive-Neuro Control of Robot Manipulator Using DSPs)

  • 이우송;차보남;김영규;김용태;한성현
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2002년도 춘계학술대회 논문집
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    • pp.573-578
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    • 2002
  • In this paper, it Is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-negro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

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디지털 시그널 프로세서를 이용한 로봇 매니퓰레이터의 적응-신경제어 (The Adaptive-Neuro Control of Robot Manipulator Using DSPs)

  • 차보남;김성일;이진;이치우;한성현
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2001년도 추계학술대회(한국공작기계학회)
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    • pp.122-127
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    • 2001
  • In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

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DSPs(TMS320C50)를 이용한 로봇 매니퓰레이터의 적응-신경제어기 실현 (Implementation of the Adaptive-Neuro Control of Robot Manipulator Using DSPs(TMS320C50))

  • 정동연;김용태;한성현
    • 한국공작기계학회:학술대회논문집
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    • 한국공작기계학회 2002년도 추계학술대회 논문집
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    • pp.256-261
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    • 2002
  • In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

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자율분산 신경회로망을 이용한 간접 적응제어 (Indirect Adaptive Control Based on Self-Organized Distributed Network(SODN))

  • 최종수;김형석;김성중;권오신
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1996년도 하계학술대회 논문집 B
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    • pp.1182-1185
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    • 1996
  • The objective of this paper is to control a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism Each local network learns only data in a subregion. Methods for indirect adaptive control of nonlinear systems using the SODN is presented. Through extensive simulation, the SODN is shown to be effective for adaptive control of nonlinear dynamic systems.

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수직다물체시스템의 오차파형전달방식 간접적응형 분산학습제어 (Indirect Adaptive Decentralized Learning Control based Error Wave Propagation of the Vertical Multiple Dynamic Systems)

  • 이수철
    • 한국산업정보학회:학술대회논문집
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    • 한국산업정보학회 2006년도 춘계 국제학술대회 논문집
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    • pp.211-217
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    • 2006
  • 반복학습제어는 특정목적 궤도의 반복작업을 수행하는 정밀도를 개선하는 제어기를 개발하는 기술이다. 기존 연구에서는 수직다물체의 반복정밀도를 개선하기 위하여 누적학습제어와 적응제어 기법을 한 반복영역에서 동시에 실시하는 기법을 개발하였다. 당초 이 기술은 생산조립라인의 산업용 로봇에서 발생하는 반복정밀도를 개선하기 위해 개발하였으며, 특히, 분산학습기법은 산업용 로봇에서 발생하는 실질적 제어 방식에 유효한 기법이다. 본 논문에서 개발한 제어기술은 한 반복영역의 모든 시간대의 입출력 정보를 동시에 학습하기 보다는 매 시간대의 입출력 정보를 각 시간대 마다 충분히 학습하고 다음 시간대의 정보를 학습하는 것이다. 본 논문에서 개발한 기술을 산업용 로봇과 의료기기에 적용하면 수직다물체의 정밀도 품질보증 확보에 큰 기여를 하게 된다.

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오차파형전달방식 간접적응형 분산학습제어 알고리즘을 적용한 수직다물체시스템의 반복정밀도 보증 (Quality Assurance of Repeatability for the Vertical Multiple Dynamic Systems in Indirect Adaptive Decentralized Learning Control based Error wave Propagation)

  • 이수철
    • 한국산업정보학회논문지
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    • 제11권2호
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    • pp.40-47
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    • 2006
  • 반복학습제어는 특정목적 궤도의 반복작업을 수행하는 정밀도를 개선하는 제어기를 개발하는 기술이다. 기존 연구에서는 수직다물체의 반복정밀도를 개선하기 위하여 누적학습제어와 적응제어 기법을 한 반복영역에서 동시에 실시하는 기법을 개발하였다. 당초 이 기술은 생산조립라인의 산업용 로봇에서 발생하는 반복정밀도를 개선하기 위해 개발하였으며, 특히, 분산학습기법은 산업용 로봇에서 발생하는 실질적 제어 방식에 유효한 기법이다 본 논문에서 개발한 제어기술은 한 반복영역의 모든 시간대의 입출력 정보를 동시에 학습하기 보다는 매 시간대의 입출력 정보를 각 시간대 마다 충분히 학습하고 다음 시간대의 정보를 학습하는 것이다. 본 논문에서 개발한 기술을 산업용 로봇과 의료기기에 적용하면 수직다물체의 정밀도 품질보증 확보에 큰 기여를 하게 된다.

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시스템의 불확실성에 대한 신경망 모델을 통한 강인한 비선형 제어 (A Robust Nonlinear Control Using the Neural Network Model on System Uncertainty)

  • 이수영;정명진
    • 대한전기학회논문지
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    • 제43권5호
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    • pp.838-847
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    • 1994
  • Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

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RBFN을 이용한 로봇 매니퓰레이터의 적응제어 방법 (An Adaptive Control Method of Robot Manipulators using RBFN)

  • 이민중;최영규;박진현
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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    • pp.420-420
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    • 2000
  • In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators The structure of the proposed controller consists of a RBFN and VSC-1 ike control. RBFN is used in order to approximate かon system, and VSC-like control to guarantee robustness On the basis of the Lyapunov stability theorem, we guarantee the stability for the total system. And the learning law of RBFN is established by the Lyapunov method, Finally, we apply the proposed controller to tracking control for a 2 link SCARA type robot manipulator.

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Self-Recurrent Wavelet Neural Network Based Direct Adaptive Control for Stable Path Tracking of Mobile Robots

  • You, Sung-Jin;Oh, Joon-Seop;Park, Jin-Bae;Choi, Yoon-Ho
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2004년도 ICCAS
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    • pp.640-645
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    • 2004
  • This paper proposes a direct adaptive control method using self-recurrent wavelet neural network (SRWNN) for stable path tracking of mobile robots. The architecture of the SRWNN is a modified model of the wavelet neural network (WNN). Unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. For this ability of the SRWNN, the SRWNN is used as a controller with simpler structure than the WNN in our on-line control process. The gradient-descent method with adaptive learning rates (ALR) is applied to train the parameters of the SRWNN. The ALR are derived from discrete Lyapunov stability theorem, which are used to guarantee the stable path tracking of mobile robots. Finally, through computer simulations, we demonstrate the effectiveness and stability of the proposed controller.

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