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• W2783106319 abstract "Many torque control approaches have been proposed for robotic devices used in legged locomotion, but few comparisons have been performed across controllers in the same system. In this study, we compared the torque-tracking performance of nine control strategies, including variations on classical feedback control, model-based control, adaptive control, and iterative learning. To account for interactions between patterns in desired torque and tracking performance, we tested each in combination with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyographic measurements. Controllers were implemented on an ankle exoskeleton with series elastic actuation driven by an off-board motor through a uni-directional Bowden cable. The exoskeleton was worn by one human subject walking on a treadmill at 1.25 m⋅s−1 for one hundred steady-state steps under each condition. We found that the combination of proportional control, damping injection and iterative learning resulted in substantially lower root-mean-squared error than other torque control approaches for all high-level controllers. With this low level torque controller, RMS errors can be as low as 1.3% of peak torque for real-time tracking, and 0.2% for the average stride. Model-free, integration-free feedback control seems to be well suited to the uncertain, changing dynamics of the human–robot system, while iterative learning is advantageous in the cyclic task of walking." @default.
• W2783106319 created "2018-01-26" @default.
• W2783106319 creator A5080753188 @default.
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• W2783106319 date "2017-01-01" @default.
• W2783106319 modified "2023-10-18" @default.
• W2783106319 title "Torque Control in Legged Locomotion ⁎ ⁎Supplementary document of this chapter is located at https://www.andrew.cmu.edu/user/shc17/Zhang_2016_BLL—SuppMat.pdf." @default.
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• W2783106319 doi "https://doi.org/10.1016/b978-0-12-803766-9.00007-5" @default.
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