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.

Automated summary

The development of biodegradable soft robotics requires an eco-friendly source of energy like Microbial Fuel Cells (MFCs), with the use of artificial intelligence methods to enhance control techniques and accurately predict their electrical output; predicting MFC outputs can assist in determining feeding intervals and quantities needed, which can be incorporated in the behavioral repertoire of a soft robot.