论文以及视频链接：https://www.sciencedirect.com/science/article/abs/pii/S0736584525002042

基于柔顺导纳控制的物理人机交互（pHRI）系统通常依赖力/力矩传感器获取意图力，但机器人运动与人手阻抗产生的阻抗力反馈会导致意图力显著失真，从而引发振动并削弱交互的透明性与稳定性。为此，本文提出了一种结合交互意图滤波器（IIF）和增强型时域振动观测器（ETDVO）的可变导纳控制策略。IIF基于实际人机协作任务中的力信号频域分析设计，用于抑制非期望的阻抗力反馈，避免其传递给导纳控制器。ETDVO则通过变宽度时间窗准确计算振动指标。结合该振动指标，本文提出了一种基于指数映射的可变导纳控制策略，通过快速调节导纳参数，实现有效振动抑制与稳定性增强。在7自由度机械臂上的人机协作激光跟踪实验验证了所提方法有效性。统计结果表明：与稳定的高增益导纳控制器相比，任务时间、能量消耗和平均力分别降低了10%、54%、58%以上，显著提升了交互透明性与整体稳定性。

J. Huang, M. Yuan* and X. Zhang. Simultaneous high transparency and robust stability-oriented physical human-robot interaction using an interaction intention filter and a vibration observer[J]. Robotics and Computer-Integrated Manufacturing, 2026, 98: 103150. DOI: 10.1016/j.rcim.2025.103150.

Abstract

Physical Human-Robot Interaction (pHRI) systems equipped with compliant admittance controllers typically utilize F/T sensors to capture the forces applied by the operator. However, the impedance force feedback generated by the robot's motion and the impedance of the human hand can significantly distort the intentional forces. This distortion can lead to vibrations that compromise both interaction transparency and stability. To address this issue, we propose a variable admittance control strategy that incorporates an Interaction Intention Filter (IIF) and an Enhanced Time-Domain Vibration Observer (ETDVO). We first introduce the concept of the IIF, which is designed based on a frequency-domain analysis of force signals collected from real-world human--robot cooperation tasks. This filter effectively prevents unintended impedance force feedback from being transmitted to the admittance controller. Moreover, to ensure interaction stability across diverse environments, we propose a variable-width time window-based ETDVO for accurately computing the vibration index. By leveraging this index, we introduce a variable admittance control strategy based on exponential mapping, which enables rapid adjustment of the admittance parameters, effectively suppresses vibrations and enhances stability. Finally, the proposed strategy is validated through human--robot cooperative laser tracking experiments conducted on a 7-DoF manipulator. Statistical results from the experiments demonstrate that our approach not only improves interaction transparency but also significantly enhances overall stability. Compared to the stable high-gain admittance controller, the Task Time,Required Energy, and Mean Force are reduced by over 10%, 54%, 58%, respectively.