ArticleFrontiers in robotics and AI2021
Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning.
Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 19 citations in OpenAlex.
- Sonomyography accurately captures joint kinematics during volitional and electrically stimulated motion in healthy adults and an individual with cerebral palsy.Journal of neuroengineering and rehabilitation · 2025Article
- Evaluating therapeutic effects of exoskeletons and FES in SCI: integrative review of the literature.Spinal cord · 2025Review
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5 authors at 3 institutions in 1 country.
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Abstract
A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network-based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton's knee motors based on the muscle fatigue and recovery characteristics of a participant's quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.
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