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Decentralized Robust Adaptive Neural Network Control for Electrically Driven Robot Manipulators with Bounded Input Voltages
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 Title & Authors
Decentralized Robust Adaptive Neural Network Control for Electrically Driven Robot Manipulators with Bounded Input Voltages
Shin, Jin-Ho; Kim, Won-Ho;
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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.
Robot Manipulator;Bounded Input Voltage;Decentralized Control;Radial Basis Function Neural Network;Robust Adaptive Control;
 Cited by
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