Model Reference Adaptive Impedance Control for Physical Human-Robot Interaction
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.
B. Alqaudi et al., "Model Reference Adaptive Impedance Control for Physical Human-Robot Interaction," Control Theory and Technology, vol. 14, no. 1, pp. 68-82, Springer Verlag (Germany), Feb 2016.
The definitive version is available at https://doi.org/10.1007/s11768-016-5138-2
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Control Systems; Controllers; Human Robot Interaction; Man Machine Systems; Robots; Human-Robot Systems; Impedance Characteristics; Impedance Control; Model Reference; Model Reference Adaptive; Neuro-Adaptive Controllers; Physical Human-Robot Interactions; Point-To-Point Motion; Model Reference Adaptive Control; Human-Robot Interaction; Model Reference Neuroadaptive
International Standard Serial Number (ISSN)
Article - Journal
© 2016 Springer Verlag (Germany), All rights reserved.
01 Feb 2016