Michail Tsompanas Antisthenis.Tsompanas@uwe.ac.uk
Lecturer in Computer Science
Michail Tsompanas Antisthenis.Tsompanas@uwe.ac.uk
Lecturer in Computer Science
Jiseon You Jiseon.You@uwe.ac.uk
Lecturer in Engineering/ Project Management
Hemma Philamore
Jonathan Rossiter
Yannis Ieropoulos Ioannis2.Ieropoulos@uwe.ac.uk
Professor in Bioenergy & Director of B-B
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.
Tsompanas, M. A., You, J., Philamore, H., Rossiter, J., & Ieropoulos, I. (2021). Neural networks predicting microbial fuel cells output for soft robotics applications. Frontiers in Robotics and AI, 8, Article 633414. https://doi.org/10.3389/frobt.2021.633414
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 28, 2021 |
Online Publication Date | Mar 4, 2021 |
Publication Date | Mar 4, 2021 |
Deposit Date | Mar 4, 2021 |
Publicly Available Date | Mar 5, 2021 |
Journal | Frontiers in Robotics and AI |
Electronic ISSN | 2296-9144 |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 8 |
Article Number | 633414 |
DOI | https://doi.org/10.3389/frobt.2021.633414 |
Public URL | https://uwe-repository.worktribe.com/output/7170242 |
Publisher URL | https://www.frontiersin.org/article/10.3389/frobt.2021.633414 |
Neural networks predicting microbial fuel cells output for soft robotics applications
(2.1 Mb)
PDF
Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Microbial fuel cells and their electrified biofilms
(2021)
Journal Article
Electrosynthesis, modulation, and self-driven electroseparation in microbial fuel cells
(2021)
Journal Article
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search