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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]
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Database: Engineering Source
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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]
ISSN:01423312
DOI:10.1177/01423312241298351