Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

Michail-Antisthenis Tsompanas, Jiseon You, Hemma Philamore, Jonathan M Rossiter, Ioannis Ieropoulos*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

16 Citations (Scopus)
75 Downloads (Pure)


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.
Original languageEnglish
Article number633414
Number of pages31
JournalFrontiers in Robotics and AI
Issue number633414
Publication statusPublished - 4 Mar 2021

Bibliographical note

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.


  • microbial fuel cells
  • soft robotics
  • neural network
  • nonlinear autoregressive network
  • robotic control


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