Spiking control of dielectric elastomer actuators

Rigid robots are highly specialised and can perform tasks with incredible precision. In contrast, soft robots provide a promising solution for creating robotic systems that are inherently better suited for unstructured and dynamic environments. Artificial muscles comprise one of the core components of soft robots. Among them, dielectric elastomer actuators (DEAs) represent the technology that comes closest to the capabilities of a natural muscle. However, their viscoelastic effects may limit the applicability and represent the main reason why suitable control methods are required. Thus, the objective of this work is to have a look at bioinspired spiking closed-loop control systems. By doing so, the research attempts to take a step towards creating true soft robots, which are bioinspired in all systems. A spiking neural network (SNN) is developed that comprised the main part of the controller and whose output is used as the control value. All information inside the controller was represented via spikes and the controller was implemented on neuromorphic hardware. During the validation, the general functionality was proven and a frequency-dependent tracking performance was observed. In a frequency range comparable to other works (≤ 0.5 Hz), the qualitative evaluation shows a good tracking performance even with a sinusoidal input.