Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements

Author:

Dunkelberger Nathan,Berning Jeffrey,Schearer Eric M.,O'Malley Marcia K.

Abstract

IntroductionIndividuals who have suffered a cervical spinal cord injury prioritize the recovery of upper limb function for completing activities of daily living. Hybrid FES-exoskeleton systems have the potential to assist this population by providing a portable, powered, and wearable device; however, realization of this combination of technologies has been challenging. In particular, it has been difficult to show generalizability across motions, and to define optimal distribution of actuation, given the complex nature of the combined dynamic system.MethodsIn this paper, we present a hybrid controller using a model predictive control (MPC) formulation that combines the actuation of both an exoskeleton and an FES system. The MPC cost function is designed to distribute actuation on a single degree of freedom to favor FES control effort, reducing exoskeleton power consumption, while ensuring smooth movements along different trajectories. Our controller was tested with nine able-bodied participants using FES surface stimulation paired with an upper limb powered exoskeleton. The hybrid controller was compared to an exoskeleton alone controller, and we measured trajectory error and torque while moving the participant through two elbow flexion/extension trajectories, and separately through two wrist flexion/extension trajectories.ResultsThe MPC-based hybrid controller showed a reduction in sum of squared torques by an average of 48.7 and 57.9% on the elbow flexion/extension and wrist flexion/extension joints respectively, with only small differences in tracking accuracy compared to the exoskeleton alone.DiscussionTo realize practical implementation of hybrid FES-exoskeleton systems, the control strategy requires translation to multi-DOF movements, achieving more consistent improvement across participants, and balancing control to more fully leverage the muscles' capabilities.

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Biomedical Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Knee extension tracking and fatigue regulation results using a robust MPC approach in a hybrid exoskeleton;Control Engineering Practice;2023-12

2. An EMG-triggered cooperative controller for a hybrid FES-robotic system;2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE);2023-10-25

3. Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

4. Combining FES and Exoskeletons in a Hybrid Haptic System for Enhancing VR Experience;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

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