Closing the Loop on Exoskeleton Motor Controllers: Benefits of Regression-Based Open-Loop Control

Greg Orekhov, Jason Luque, Zachary F. Lerner

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Lower-limb exoskeletons are widely researched to improve walking performance and mobility. Low-level sensor-less exoskeleton motor control is attractive for consumer applications due to reduced device complexity and cost, but complex and variable transmission system configurations make the development of effective open-loop motor controllers that are responsive to user input challenging. The objective of this study was to develop and validate an open-loop motor control framework resulting in similar or greater performance vs. closed-loop torque control. We used generalized linear regression to develop two open-loop controllers by modeling motor current during exoskeleton-assisted walking; a 'complex' model used desired torque and estimated ankle angular velocity as inputs, while a 'simple' model used desired torque alone. Five participants walked at 1.0-1.3 m/s on a treadmill with closed-loop and both open-loop controllers providing ankle exoskeleton assistance. Both open-loop current controllers had similar root-mean-squared torque tracking error (p = 0.23) compared to the closed-loop torque-feedback controller. Both open-loop controllers had improved relative average torque production (p < 0.001 complex, p = 0.022 simple), lower power consumption (p < 0.001 for both), and reduced operating noise (p = 0.002 complex, p < 0.001 simple) over the closed-loop controller. New control models developed for a different ankle exoskeleton configuration showed similar improvements (lower torque error, greater average and peak torque production, lower power consumption) over closed-loop control during over-ground walking. These results demonstrate that our framework can produce open-loop motor controllers that match closed-loop control performance during exoskeleton operation.

Original languageEnglish (US)
Article number9146208
Pages (from-to)6025-6032
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - Oct 2020
Externally publishedYes


  • Adaptive control
  • ankle assistance
  • closed loop
  • exoskeleton
  • open loop
  • statistical modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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