Self-sensing in dielectric electro-active polymer actuator using linear-in-parametes online estimation

This paper presents a self-sensing methodology for Dielectric ElectroActive Polymer actuators. The proposed approach is based on using DEAP voltage and current to estimate electrical resistance and capacitance, and using the latter to reconstruct the actuator deformation. For the estimation of the electrical parameters, the performance of two standard linear regression algorithms are compared, i.e. standard Least Mean Squares (LMS) and Recursive Least Squares (RLS). Some filtering techniques are also suggested in order to improve the quality of the estimation. The full algorithm is first illustrated in detail and then validated on an experimental actuator prototype, consisting in a DEAP membrane combined with a bi-stable biasing element which enables large actuation stroke.