Skip to main content
  Cornell University

MAE Publications and Papers

Sibley School of Mechanical and Aerospace Engineering

New article: Improving Actuation Efficiency Through Variable Recruitment Hydraulic McKibben Muscles: modeling, orderly recruitment control, and experiments

Article:  Meller, M; Chipka, J; Volkov, A; Bryant, M; Garcia, E; “Improving Actuation Efficiency Through Variable Recruitment Hydraulic McKibben Muscles: modeling, orderly recruitment control, and experiments”, Bioinspiration & Biomimetics, 11 (6)

DOI

Abstract:  Hydraulic control systems have become increasingly popular as the means of actuation for human-scale legged robots and assistive devices. One of the biggest limitations to these systems is their run time untethered from a power source. One way to increase endurance is by improving actuation efficiency. We investigate reducing servovalve throttling losses by using a selective recruitment artificial muscle bundle comprised of three motor units. Each motor unit is made up of a pair of hydraulic McKibben muscles connected to one servovalve. The pressure and recruitment state of the artificial muscle bundle can be adjusted to match the load in an efficient manner, much like the firing rate and total number of recruited motor units is adjusted in skeletal muscle. A volume-based effective initial braid angle is used in the model of each recruitment level. This semi-empirical model is utilized to predict the efficiency gains of the proposed variable recruitment actuation scheme versus a throttling-only approach. A real-time orderly recruitment controller with pressure-based thresholds is developed. This controller is used to experimentally validate the model-predicted efficiency gains of recruitment on a robot arm. The results show that utilizing variable recruitment allows for much higher efficiencies over a broader operating envelope.

Funding Acknowledgement:  Defense Advanced Research Projects Agency (DARPA) through the Maximum Mobility and Manipulation (M3) Program [W31P4Q-13-1-0012]

Funding Text:  The authors gratefully acknowledge the Defense Advanced Research Projects Agency (DARPA) for financial support through the Maximum Mobility and Manipulation (M3) Program under the direction of Dr Gill Pratt (grant number W31P4Q-13-1-0012). The authors would also like to thank Julian Whitman and Joval Mathew for helping construct the robot arm experiment setup.

Skip to toolbar