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Neural networks predicting microbial fuel cells output for soft robotics applications

Tsompanas, Michail Antisthenis; You, Jiseon; Philamore, Hemma; Rossiter, Jonathan; Ieropoulos, Ioannis

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Authors

Jiseon You Jiseon.You@uwe.ac.uk
Senior Lecturer in Engineering/ Project Management

Hemma Philamore

Jonathan Rossiter

Yannis Ieropoulos Ioannis2.Ieropoulos@uwe.ac.uk
Professor in Bioenergy & Director of B-B



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.

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

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