Finite-time Convergence Neural Network based Force-Motion Control for Unknown Surface with Orientation Compliance
Xu, Zhihao; Li, Yuming; Liao, Zhaoyang; Li, Shuai; Zhang, Fuyong; Zhou, Xuefeng; Wu, Hongmin; Yang, Chenguang (2025-11-26)
IEEE
26.11.2025
Z. Xu et al., "Finite-Time Convergence Neural Network-Based Force-Motion Control for Unknown Surface With Orientation Compliance," in IEEE Transactions on Automation Science and Engineering, vol. 23, pp. 564-576, 2026, doi: 10.1109/TASE.2025.3637540.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202606013831
https://urn.fi/URN:NBN:fi:oulu-202606013831
Tiivistelmä
Abstract
In this paper, an adaptive force-motion control framework with orientation compliance is present for redundant manipulators in physical interaction with unknown surfaces. The proposed framework includes control task space definition and double-closed-loop control based on external force loop approach. Firstly, a specification matrix is designed merely through force feedback to ensure the control task space defined in orthogonal spaces. Then, an orientation compliance controller and a force-motion close-loop controller are constructed in the outer-loop control of external force feedback loop approach. Secondly, the output of outer-loop control task, along with boundary constraints and optimization indexes is formulated as a nonlinear dynamic programming problem. Next a finite-time convergence neural network based inner-loop controller is proposed for this category of dynamic programming problem and its stability and convergence analysis are given. Simulations verify the convergence and effectiveness of the proposed framework. The real-world experiments show that the Mean Integral of the Absolute Error of the proposed control framework is reduced by 77.26% compared with constant impedance control. Note to Practitioners—Existing control methodologies exhibit limited adaptability to uncertainties stemming from unknown surfaces. Therefore, this paper proposes a force-motion control framework with orientation compliance. Within the proposed framework, the task space is orthogonally decomposed into the motion control space, force control space, and orientation control space. The desired orientation is derived from the feedback signals acquired by the force sensor, and the orientation error is characterized in the form of quaternions. In the force control space, the stability of force control is enhanced by incorporating an integral term into constant impedance control. Finally, a finite-time convergent neural network based inner-loop controller is designed to handle optimization indexes and boundary constraints. The simulation and experimental results demonstrate that the proposed framework is capable of addressing force-motion control and orientation compliance interacting with unknown surfaces.
In this paper, an adaptive force-motion control framework with orientation compliance is present for redundant manipulators in physical interaction with unknown surfaces. The proposed framework includes control task space definition and double-closed-loop control based on external force loop approach. Firstly, a specification matrix is designed merely through force feedback to ensure the control task space defined in orthogonal spaces. Then, an orientation compliance controller and a force-motion close-loop controller are constructed in the outer-loop control of external force feedback loop approach. Secondly, the output of outer-loop control task, along with boundary constraints and optimization indexes is formulated as a nonlinear dynamic programming problem. Next a finite-time convergence neural network based inner-loop controller is proposed for this category of dynamic programming problem and its stability and convergence analysis are given. Simulations verify the convergence and effectiveness of the proposed framework. The real-world experiments show that the Mean Integral of the Absolute Error of the proposed control framework is reduced by 77.26% compared with constant impedance control. Note to Practitioners—Existing control methodologies exhibit limited adaptability to uncertainties stemming from unknown surfaces. Therefore, this paper proposes a force-motion control framework with orientation compliance. Within the proposed framework, the task space is orthogonally decomposed into the motion control space, force control space, and orientation control space. The desired orientation is derived from the feedback signals acquired by the force sensor, and the orientation error is characterized in the form of quaternions. In the force control space, the stability of force control is enhanced by incorporating an integral term into constant impedance control. Finally, a finite-time convergent neural network based inner-loop controller is designed to handle optimization indexes and boundary constraints. The simulation and experimental results demonstrate that the proposed framework is capable of addressing force-motion control and orientation compliance interacting with unknown surfaces.
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