Neural networks predicting microbial fuel cells output for soft robotics applications
Neural networks predicting microbial fuel cells output for soft robotics applications
The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.
microbial fuel cells, neural network, nonlinear autoregressive network, robotic control, soft robotics
Tsompanas, Michail Antisthenis
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You, Jiseon
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Philamore, Hemma
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Rossiter, Jonathan
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Ieropoulos, Ioannis
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4 March 2021
Tsompanas, Michail Antisthenis
d94143ce-e72b-4909-9511-25930df12bd1
You, Jiseon
1442df08-0ea4-4134-b6be-6b773b05f58d
Philamore, Hemma
d0a1cf2b-226d-4600-ae34-d220a1cc3767
Rossiter, Jonathan
64caa0df-19e0-40c8-ab69-7021de665c39
Ieropoulos, Ioannis
6c580270-3e08-430a-9f49-7fbe869daf13
Tsompanas, Michail Antisthenis, You, Jiseon, Philamore, Hemma, Rossiter, Jonathan and Ieropoulos, Ioannis
(2021)
Neural networks predicting microbial fuel cells output for soft robotics applications.
Frontiers in Robotics and AI, 8, [633414].
(doi:10.3389/frobt.2021.633414).
Abstract
The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot.
Text
frobt-08-633414
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More information
Accepted/In Press date: 28 January 2021
Published date: 4 March 2021
Additional Information:
Funding Information:
This work was funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 686585.
Publisher Copyright:
© Copyright © 2021 Tsompanas, You, Philamore, Rossiter and Ieropoulos.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords:
microbial fuel cells, neural network, nonlinear autoregressive network, robotic control, soft robotics
Identifiers
Local EPrints ID: 454832
URI: http://eprints.soton.ac.uk/id/eprint/454832
PURE UUID: ca718525-ec06-462c-9a0b-805926c1ef03
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Date deposited: 24 Feb 2022 21:55
Last modified: 18 Mar 2024 04:04
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Contributors
Author:
Michail Antisthenis Tsompanas
Author:
Jiseon You
Author:
Hemma Philamore
Author:
Jonathan Rossiter
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