Imperial College London
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DrEnricoFranco

Faculty of Engineering, Department of Mechanical Engineering

Lecturer
 
 
 
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Contact

 

e.franco11 Website CV

 
 
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Location

 

414BBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Caulcrick:2021:10.1109/TMRB.2021.3105141,
author = {Caulcrick, C and Huo, W and Franco, E and Mohammed, S and Hoult, W and Vaidyanathan, R},
doi = {10.1109/TMRB.2021.3105141},
journal = {IEEE Transactions on Medical Robotics and Bionics},
pages = {980--991},
title = {Model predictive control for human-centred lower limb robotic assistance},
url = {http://dx.doi.org/10.1109/TMRB.2021.3105141},
volume = {3},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Loss of mobility and/or balance resulting from neural trauma is a critical public health issue. Robotic exoskeletons hold great potential for rehabilitation and assisted movement. However, the synergy of robot operation with human effort remains a problem. In particular, optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons that achieves on-the-fly transitions between modes of assistance. The architecture implements a fuzzy logic algorithm (FLA) to map key modes of assistance based on human involvement. Three modes are utilised: passive, for human relaxed and robot dominant; active-assist, for human cooperation with the task; and safety, in the case of human resistance to the robot. Electromyography (EMG) signals are further employed to predict the human torque. EMG output is used by the MPC for trajectory following and by the FLA for decision making. Experimental validation using a 1-DOF knee exoskeleton demonstrates the controller tracking a sinusoidal trajectory with relaxed, assistive, and resistive operational modes. Results demonstrate rapid and appropriate transfers among the assistance modes, and satisfactory AAN performance in each case, offering a new level of human-robot synergy for mobility assist and rehabilitation.
AU  - Caulcrick,C
AU  - Huo,W
AU  - Franco,E
AU  - Mohammed,S
AU  - Hoult,W
AU  - Vaidyanathan,R
DO  - 10.1109/TMRB.2021.3105141
EP  - 991
PY  - 2021///
SN  - 2576-3202
SP  - 980
TI  - Model predictive control for human-centred lower limb robotic assistance
T2  - IEEE Transactions on Medical Robotics and Bionics
UR  - http://dx.doi.org/10.1109/TMRB.2021.3105141
UR  - http://arxiv.org/abs/2011.05079v1
UR  - https://ieeexplore.ieee.org/document/9514611
UR  - http://hdl.handle.net/10044/1/94556
VL  - 3
ER  -
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