Self-sensing at low sampling-to-signal frequency ratio: An improved algorithm for dielectric elastomer actuators

This paper presents a new self-sensing algorithm for Dielectric Elastomer actuators. The method allows to obtain accurate estimations of material capacitance and electrodes resistance from voltage and current measurements, by means of online identification algorithms, e.g., RLS. While the capacitance permits to reconstruct the actuator displacement (self-sensing), the resistance can be used to extract further information on the actuator state, e.g., fatigue (self-monitoring). The new self-sensing method is presented and compared with a different algorithm previously developed by the authors. Simulations and experiments show how capacitance and resistance predicted by the new algorithm are in agreement with the values measured with an LCR meter. Moreover, it is shown how the accuracy of the new method does not deteriorate when reducing the sampling-to-signal frequency ratio (the method is tested up to a ratio of 2.5). This result enables achieving reliable self-sensing without a significant amount of online computation effort.