2015 IEEE International Conference on Robotics and Automation (ICRA) 2015
DOI: 10.1109/icra.2015.7139980
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Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Abstract: Abstract-Few comparisons have been performed across torque controllers for exoskeletons, and differences among devices have made interpretation difficult. In this study, we compared the torque-tracking performance of nine control methods, including variations on classical feedback control, modelbased control, adaptive control and iterative learning. Each was tested with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyography. Controllers… Show more

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Cited by 102 publications
(132 citation statements)
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“…Rapidly decreasing torque during the plantarflexion phase of the BiOM ® T2 emulation proved challenging for this simple proportional control scheme, so desired ankle torque was adjusted with an iteratively learned torque ( τ a,lrn ) to compensate for steady-state errors (inspired by [13]).…”
Section: Methodsmentioning
confidence: 99%
“…Rapidly decreasing torque during the plantarflexion phase of the BiOM ® T2 emulation proved challenging for this simple proportional control scheme, so desired ankle torque was adjusted with an iteratively learned torque ( τ a,lrn ) to compensate for steady-state errors (inspired by [13]).…”
Section: Methodsmentioning
confidence: 99%
“…During walking trials, an additional time-based iterative learning term was added, which provided feed-forward compensation of torque errors that tended to occur at the same time each step. This method is described in detail in [24].…”
Section: B Controlmentioning
confidence: 99%
“…A tethered emulator approach [20][21][22][23] decouples the problems of discovering desirable prosthesis functionality from the challenges of developing fully autonomous systems. Powerful off-board motors and controllers are connected to lightweight instrumented end-effectors via flexible tethers, resulting in low worn mass and high-fidelity torque control [20,21,24]. Such systems can be used to haptically render virtual prostheses to human users, facilitating the discovery of novel device behaviors [25] that can then be embedded in separate autonomous designs.…”
Section: Introductionmentioning
confidence: 99%
“…A value of of 0.5 was found to provide a reasonable learning rate, for which the learned friction compensation profile was updated such that a new learned behavior was fully incorporated in approximately 10 seconds. Similar iterative learning methods have been successful in wearable robotics in the presence of cable frictional losses [15].…”
Section: Stance Phase Torquementioning
confidence: 99%