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Sub-optimally Solving Actuator Redundancy in a Hybrid Neuroprosthetic System with a Multi-layer Neural Network

Xuefeng Bao¹, Zhi-Hong Mao², Paul Munro²

¹Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA,USA 15261.

International Journal of Intelligent Robotics and Applications

|December 7, 2020

Summary

This study introduces a novel hybrid neuroprosthesis combining functional electrical stimulation (FES) and powered exoskeletons for paraplegia. The system optimally allocates FES and exoskeleton torque to minimize muscle fatigue and improve control.

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Area of Science:

Biomedical Engineering
Neuroscience
Robotics

Background:

Functional electrical stimulation (FES) is explored as a supplementary torque assist for lower-limb powered exoskeletons in individuals with paraplegia.
Hybrid neuroprostheses integrate FES-assist and exoskeleton torques for standing and walking.
Actuator redundancy necessitates optimal allocation of FES and exoskeleton torque to balance muscle fatigue and tracking errors.

Purpose of the Study:

To develop and validate a novel control strategy for hybrid neuroprostheses that optimally allocates FES and exoskeleton torque.
To address the challenge of accurately modeling musculoskeletal responses to FES for traditional optimal control.
To minimize muscle fatigue and tracking errors in FES-assisted exoskeleton systems.

Main Methods:

A novel identification and control structure utilizing a recurrent neural network (RNN) for system dynamics identification and feedforward neural networks (FNNs) for control.
Supervised learning trains the RNN, while reinforcement learning trains the FNNs for sub-optimal control actions.
FNNs incorporate unique output layer activation functions to manage asymmetric FES constraints and symmetric exoskeleton motor control inputs.

Main Results:

The RNN successfully identified system dynamics, enabling effective control.
The FNNs provided sub-optimal control actions, demonstrating the feasibility of the reinforcement learning approach.
Experimental validation on a seated participant with a single-joint hybrid neuroprosthesis confirmed the system's efficacy.

Conclusions:

The proposed RNN and FNN-based control structure offers a viable solution for optimal torque allocation in hybrid neuroprostheses.
This approach effectively addresses the challenges of modeling complex musculoskeletal dynamics and actuator redundancy.
The validated system shows promise for enhancing mobility and reducing muscle fatigue in individuals with paraplegia using FES-assisted exoskeletons.