Concurrent Learning Control for Treadmill Walking using a Cable-driven Exoskeleton with FES

Jonathan Casas, Chen Hao Chang, Victor H. Duenas

Research output: Chapter in Book/Entry/PoemConference contribution

2 Scopus citations


Hybrid exoskeletons integrate powered exoskeletons and functional electrical stimulation (FES) to restore limb function and improve muscle capacity. However, technical challenges exist to customize the control of hybrid devices due to the nonlinear, uncertain gait and muscle dynamics of the human-machine system. Different from optimization techniques for gait control that leverage extensive model knowledge, this paper exploits a learning-based adaptive strategy to provide torque assistance about the hip and knee joints using a cable-driven exoskeleton with FES for treadmill walking. The human-machine system is modeled with phase-dependent switched pendular dynamics to capture gait phase transitions. A concurrent learning adaptive controller is designed to estimate a subset of the uncertain leg parameters during the swing phase to improve gait control. A sliding-mode controller provides robust leg support during stance. Stability of the overall switched system is proven using a multiple Lyapunov approach and dwell time analysis to guarantee exponential tracking and parameter estimation convergence across gait phase transitions.

Original languageEnglish (US)
Title of host publication2022 American Control Conference, ACC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665451963
StatePublished - 2022
Event2022 American Control Conference, ACC 2022 - Atlanta, United States
Duration: Jun 8 2022Jun 10 2022

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2022 American Control Conference, ACC 2022
Country/TerritoryUnited States


  • Adaptive Control
  • Concurrent Learning
  • Switching systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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