© The Authors. ; Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model. ; This work has been done with the financial support of the Ministry of Science and Innovation of Spain, project HYPER (CSD 2009-00067 Hybrid Neuroprosthetic and Neurorobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders).
Feedback Error Learning Controller for Functional Electrical Stimulation Assistance in a Hybrid Robotic System for Reaching Rehabilitation
2016-01-01
2037-7460
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
REACHING AND GRASPING TRAINING BASED ON ROBOTIC HYBRID ASSISTANCE FOR NEUROLOGICAL PATIENTS
BASE | 2016
|BASE | 2013
|BASE | 2017
|BASE | 2019
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