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Sub-optimally solving actuator redundancy in a hybrid neuroprosthetic system with a multi-layer neural network structure

  • Xuefeng Bao
  • Zhi-Hong Mao
  • Paul Munro
  • Ziyue Sun
  • Nitin SharmaEmail author
Regular Paper
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Abstract

Functional electrical stimulation (FES) has recently been proposed as a supplementary torque assist in lower-limb powered exoskeletons for persons with paraplegia. In the combined system, also known as a hybrid neuroprosthesis, both FES-assist and the exoskeleton act to generate lower-limb torques to achieve standing and walking functions. Due to this actuator redundancy, we are motivated to optimally allocate FES-assist and exoskeleton torque based on a performance index that penalizes FES overuse to minimize muscle fatigue while also minimizing regulation or tracking errors. Traditional optimal control approaches need a system model to optimize; however, it is often difficult to formulate a musculoskeletal model that accurately predicts muscle responses due to FES. In this paper, we use a novel identification and control structure that contains a recurrent neural network (RNN) and several feedforward neural networks (FNNs). The RNN is trained by supervised learning to identify the system dynamics, while the FNNs are trained by a reinforcement learning method to provide sub-optimal control actions. The output layer of each FNN has its unique activation functions, so that the asymmetric constraint of FES and the symmetric constraint of exoskeleton motor control input can be realized. This new structure is experimentally validated on a seated human participant using a single joint hybrid neuroprosthesis.

Keywords

Hybrid neuroprosthesis Actuator redundancy Rehabilitation Neural network Reinforcement learning 

Notes

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering and Materials ScienceUniversity of PittsburghPittsburghUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  3. 3.Department of BioengineeringUniversity of PittsburghPittsburghUSA

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