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Decentralized variable impedance control of modular robot manipulators with physical human-robot interaction using Gaussian process-based motion intention estimation
参考中译:使用基于高斯过程的运动意图估计的具有物理人与机器人交互的模块化机器人机械手的分散变阻抗控制

刊名：	Neural Computing & Applications
作者：	Bo Dong（Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology） Shijie Li（Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology） Tianjiao An（Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology） Yiming Cui（Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology） Xinye Zhu（Key Laboratory of Advanced Structural Materials, Ministry of Education, Changchun University of Technology）
刊号：	738E0033
ISSN：	0941-0643
出版年：	2024
年卷期：	2024, vol.36, no.12
页码：	6757-6769
总页数：	13
分类号：	TP18
关键词：	Variable impedance control；Modular robot manipulators；Motion intention estimation；Decentralized control；Physical human-robot interaction
参考中译：	可变阻抗控制;模块化机器人机械手;运动意图估计;分散控制;物理人与机器人交互
语种：	eng
文摘：	This paper proposes a decentralized variable impedance control method of modular robot manipulators (MRM) with physical human-robot interaction (pHRI) using Gaussian process-based motion intention estimation. The dynamic model of MRM subsystem is established by using joint torque feedback (JTF) technique. Human limb dynamic model is regarded as mechanical impedance model, and human motion intention is estimated online based on Gaussian process. A variable impedance control method is proposed to make the MRM comply with human motion intention in the process of pHRI. A decentralized sliding mode control strategy is designed to achieve high performance position tracking and compensate the uncertainty of the controller. Based on Lyapunov theory, the uniform ultimately bounded of tracking error of each joint is proved. Finally, the effectiveness of the proposed control method under pHRI is verified by experiments. The experimental results show that the proposed method reduces the position tracking error by ～10% and the interaction force by ～20% compared with the existing control methods.
参考中译：	提出了一种基于高斯过程运动意图估计的模块化机器人机械手(MRM)的分散变阻抗控制方法。利用联合力矩反馈技术建立了磁流变子系统的动力学模型。将人体肢体动力学模型视为机械阻抗模型，基于高斯过程在线估计人体运动意图。提出了一种变阻抗控制方法，使磁流变液在PHRI过程中符合人体运动意图。为了实现高性能的位置跟踪和补偿控制器的不确定性，设计了一种分散滑模控制策略。基于李亚普诺夫理论，证明了各关节跟踪误差的一致终极有界。最后，通过实验验证了该控制方法在PHRI下的有效性。实验结果表明，与已有的控制方法相比，该方法的位置跟踪误差减小了10%，相互作用力减小了20%。