• Title/Summary/Keyword: 방사형 기저 함수 신경망

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RBFNN Based Decentralized Adaptive Tracking Control Using PSO for an Uncertain Electrically Driven Robot System with Input Saturation (입력 포화를 가지는 불확실한 전기 구동 로봇 시스템에 대해 PSO를 이용한 RBFNN 기반 분산 적응 추종 제어)

  • Shin, Jin-Ho;Han, Dae-Hyun
    • Journal of the Institute of Convergence Signal Processing
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    • v.19 no.2
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    • pp.77-88
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    • 2018
  • This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

Decentralized Robust Adaptive Neural Network Control for Electrically Driven Robot Manipulators with Bounded Input Voltages (제한된 입력 전압을 갖는 전기 구동 로봇 매니퓰레이터에 대한 분산 강인 적응 신경망 제어)

  • Shin, Jin-Ho;Kim, Won-Ho
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.25 no.11
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    • pp.753-763
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    • 2015
  • This paper proposes a decentralized robust adaptive neural network control scheme using multiple radial basis function neural networks for electrically driven robot manipulators with bounded input voltages in the presence of uncertainties. The proposed controller considers both robot link dynamics and actuator dynamics. Practically, the controller gain coefficients applied at each joint may be nonlinear time-varying and the input voltage at each joint is saturated. The proposed robot controller overcomes the various uncertainties and the input voltage saturation problem. The proposed controller does not require any robot and actuator parameters. The adaptation laws of the proposed controller are derived by using the Lyapunov stability analysis and the stability of the closed-loop control system is guaranteed. The validity and robustness of the proposed control scheme are verified through simulation results.