Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning.
| Autor: | Molazadeh V; Department of Mechanical Engineering and Material Science, University of Pittsburgh, Pittsburgh, PA, United States., Zhang Q; Neuromuscular Control and Robotics Lab, Joint Department of Biomedical Engineering, North Carolina State University and the University of North Carolina Chapel-Hill, Raleigh, NC, United States., Bao X; Department of Biomedical Engineering at University of Wisconsin-Milwaukee, Milwaukee, WI, United States., Dicianno BE; Department of Physical Medicine and Rehabilitation, School of Medicine and Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States., Sharma N; Neuromuscular Control and Robotics Lab, Joint Department of Biomedical Engineering, North Carolina State University and the University of North Carolina Chapel-Hill, Raleigh, NC, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in robotics and AI [Front Robot AI] 2021 Nov 03; Vol. 8, pp. 711388. Date of Electronic Publication: 2021 Nov 03 (Print Publication: 2021). |
| DOI: | 10.3389/frobt.2021.711388 |
| Abstrakt: | 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Molazadeh , Zhang , Bao , Dicianno and Sharma .) |
| Databáze: | MEDLINE |
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