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Adaptive-constrained admittance control for physical human–robot interaction.

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Title: Adaptive-constrained admittance control for physical human–robot interaction.
Authors: Chen, Juntong1 (AUTHOR), Dong, Zi-Yuan2 (AUTHOR), Shi, Shuanwu2 (AUTHOR), Wei, Yan2 (AUTHOR), Yu, Xinyi2 (AUTHOR), Ou, Linlin2 (AUTHOR) linlinou@zjut.edu.cn
Source: Transactions of the Institute of Measurement & Control. Apr2026, Vol. 48 Issue 7, p1223-1237. 15p.
Subjects: Human-robot interaction, Constraint algorithms, Robotic trajectory control, Lyapunov functions, Adaptive control systems, Radial basis functions
Abstract: In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments. [ABSTRACT FROM AUTHOR]
Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192584557
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  Data: Adaptive-constrained admittance control for physical human–robot interaction.
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  Data: <searchLink fieldCode="JN" term="%22Transactions+of+the+Institute+of+Measurement+%26+Control%22">Transactions of the Institute of Measurement & Control</searchLink>. Apr2026, Vol. 48 Issue 7, p1223-1237. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Human-robot+interaction%22">Human-robot interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Constraint+algorithms%22">Constraint algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+trajectory+control%22">Robotic trajectory control</searchLink><br /><searchLink fieldCode="DE" term="%22Lyapunov+functions%22">Lyapunov functions</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink>
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  Data: In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Transactions of the Institute of Measurement & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/01423312241298351
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        Text: English
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        PageCount: 15
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      – SubjectFull: Human-robot interaction
        Type: general
      – SubjectFull: Constraint algorithms
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      – SubjectFull: Robotic trajectory control
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      – SubjectFull: Lyapunov functions
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      – SubjectFull: Adaptive control systems
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      – SubjectFull: Radial basis functions
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      – TitleFull: Adaptive-constrained admittance control for physical human–robot interaction.
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            NameFull: Chen, Juntong
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            NameFull: Dong, Zi-Yuan
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              Text: Apr2026
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              Y: 2026
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