Model reference adaptive impedance control for physical human-robot interaction
Bakur ALQAUDI
Hamidreza MODARES
Isura RANATUNGA
Shaikh M TOUSIF
Frank L LEWIS
Dan O POPA
摘要：This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.
机标关键词：task performanceadaptive controlhuman operatorinteraction system
资助基金：The work was supported by the National Science Foundation(IIS-1208623)the Office of Naval Research grant(N00014-13-1-0562)the U.S. Army Research Office grant(W911NF-11-D-0001)
论文发表日期：2016-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：15( 68-82 )
英文信息