School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
ISA Trans. 2023 Jul;138:151-159. doi: 10.1016/j.isatra.2023.02.021. Epub 2023 Feb 20.
The existing model-based impedance learning control methods can provide variable impedance regulation for physical human-robot interaction (PHRI) in repetitive tasks without interactive force sensing, however, these methods require the completion of the repetitive tasks with constant time, which restricts their applications. For PHRI in repetitive tasks with different completion time, this paper proposes a spatial hybrid adaptive impedance learning control (SHAILC) strategy by using the spatial periodic characteristics of the tasks. In the spatial hybrid adaptation, spatial periodic adaptation is used for estimating time-varying human impedance and differential adaptation is designed for estimating robotic constant unknown parameters. The use of deadzone modifications in hybrid adaptation maintains the accuracy of the parameter estimation when the tracking error is small relative to the modeling error. The control stability is analyzed by a Lyapunov-based analysis in the spatial domain, and the control effectiveness and superiority is illustrated on a parallel robot in repetitive tasks with different task completion time.
现有的基于模型的阻抗学习控制方法可以在无需交互力感测的情况下为物理人机交互（PHRI）在重复任务中提供可变阻抗调节，但是，这些方法需要以恒定时间完成重复任务，这限制了它们的应用。对于具有不同完成时间的重复 PHRI 任务，本文提出了一种空间混合自适应阻抗学习控制（SHAILC）策略，该策略利用了任务的空间周期性特征。在空间混合自适应中，使用空间周期性自适应来估计时变人体阻抗，并且设计了微分自适应来估计机器人的常数未知参数。在混合自适应中使用死区修改可以在跟踪误差相对于建模误差较小时保持参数估计的准确性。通过在空间域中的基于 Lyapunov 的分析来分析控制稳定性，并在具有不同任务完成时间的重复任务中使用并联机器人来说明控制的有效性和优越性。
