Variable Damping Control for pHRI: Considering Stability, Agility, and Human Effort in Controlling Human Interactive Robots

• Published in: IEEE transactions on human-machine systems Vol. 51; no. 5; pp. 504 - 513
• Main Authors: Zahedi, Fatemeh, Arnold, James, Phillips, Connor, Lee, Hyunglae
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agility; Control stability; Controllers; Damping; End effectors; Energy dissipation; Extremities; human effort; Human engineering; Human-robot interaction; Impedance; Interactive control; Mathematical analysis; Muscles; Performance measurement; physical human robot interaction; Rehabilitation; Robot arms; Stability; Tradeoffs; Tuning; variable impedance control
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2021.3090064

This article presents a multi-degree-of-freedom variable damping controller to manage the trade-off between stability and agility and to reduce user effort in physical human-robot interaction. The controller accounts for the human body's inherent impedance properties and applies a range of robotic damping from negative (energy injection) to positive (energy dissipation) values based on the user's intent of motion. To evaluate the effectiveness of the proposed controller in balancing the trade-off between stability/agility and reducing user effort, two studies are performed on both the human upper-extremity and lower-extremity to represent both industrial and rehabilitation applications of the proposed controller. These studies required subjects to perform a series of multidimensional target reaching tasks while the human user interacted with either the end-effector of a robotic arm for the upper-extremity study or a wearable ankle robot for the lower-extremity study. Stability, agility, and user effort are quantified by a variety of performance metrics. Stability is quantified by both overshoot and stabilization time. Mean and maximum speed are used to quantify agility. To quantify the user effort, both overall and maximum muscle activation, and mean and maximum root-mean-squared interaction force are calculated. The results of both the upper- and lower-extremity studies demonstrate that the controller is able to reduce user effort while increasing agility at a negligible cost to stability.