.png){kind=icon}
返回
Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3243091.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration-dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.
Keyword:
Impedance control
impedance learning
iterative learning
physical human-robot interaction (PHRI)
repetitive learning control
robot control
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
Modeling Merging Acceleration and Deceleration Behavior Based on Gradient-Boosting Decision Tree
PREAI
分享收藏An Active Repetitive Learning Control Method for Lateral Suspension Systems of High-Speed Trains
PREAI
分享收藏20.7 A 0.45-to-3V reconfigurable charge-pump energy harvester with two-dimensional MPPT for Internet of Things20.7一种0.45 to-3V的物联网二维MPPT可重构电荷泵能量采集器
OAAI
OAAI分享收藏
查看更多内容