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    Neural Network-Based Region Tracking Control for a Flexible-Joint Robot Manipulator

    Source: Journal of Computational and Nonlinear Dynamics:;2023:;volume( 019 ):;issue: 002::page 21003-1
    Author:
    Yu, Jinwei
    ,
    Wu, Mengyang
    ,
    Ji, Jinchen
    ,
    Yang, Weihua
    DOI: 10.1115/1.4064201
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The present paper proposes a neural network-based adaptive region-tracking control strategy for a flexible-joint robot manipulator subjected to region constraints. The developed neural network-based control strategy can globally stabilize the robot manipulator and cope with model uncertainties and the external unknown bounded disturbances. Different from the existing literature, by using the sliding mode technology and the singular perturbation theory, the developed control strategy does not require the high-order derivatives of the link states such as jerk and acceleration since the high-order derivative information is not always available in practical applications. By using Lyapunov stability theory, it is proved that the proposed neural network-based control strategy can guarantee that all the parameter variables in the closed-loop system are bounded, and the flexible-joint robot manipulator with unknown dynamics can reach inside the dynamic region and also maintain the velocity matching with the desired moving region. Since the assumption of linearization of the unknown dynamic parameters is removed, the proposed control strategy does not require the calculation of the complex regression matrix. Therefore, the proposed method has great robustness and the ability of model generalization. Simulations are given to demonstrate the validity of the proposed control strategy.
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      Neural Network-Based Region Tracking Control for a Flexible-Joint Robot Manipulator

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4295841
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    contributor authorYu, Jinwei
    contributor authorWu, Mengyang
    contributor authorJi, Jinchen
    contributor authorYang, Weihua
    date accessioned2024-04-24T22:46:12Z
    date available2024-04-24T22:46:12Z
    date copyright12/20/2023 12:00:00 AM
    date issued2023
    identifier issn1555-1415
    identifier othercnd_019_02_021003.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295841
    description abstractThe present paper proposes a neural network-based adaptive region-tracking control strategy for a flexible-joint robot manipulator subjected to region constraints. The developed neural network-based control strategy can globally stabilize the robot manipulator and cope with model uncertainties and the external unknown bounded disturbances. Different from the existing literature, by using the sliding mode technology and the singular perturbation theory, the developed control strategy does not require the high-order derivatives of the link states such as jerk and acceleration since the high-order derivative information is not always available in practical applications. By using Lyapunov stability theory, it is proved that the proposed neural network-based control strategy can guarantee that all the parameter variables in the closed-loop system are bounded, and the flexible-joint robot manipulator with unknown dynamics can reach inside the dynamic region and also maintain the velocity matching with the desired moving region. Since the assumption of linearization of the unknown dynamic parameters is removed, the proposed control strategy does not require the calculation of the complex regression matrix. Therefore, the proposed method has great robustness and the ability of model generalization. Simulations are given to demonstrate the validity of the proposed control strategy.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleNeural Network-Based Region Tracking Control for a Flexible-Joint Robot Manipulator
    typeJournal Paper
    journal volume19
    journal issue2
    journal titleJournal of Computational and Nonlinear Dynamics
    identifier doi10.1115/1.4064201
    journal fristpage21003-1
    journal lastpage21003-9
    page9
    treeJournal of Computational and Nonlinear Dynamics:;2023:;volume( 019 ):;issue: 002
    contenttypeFulltext
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