Advanced modeling techniques for electroactive polymers transducers

Abstract

This dissertation deals with the modeling of electroactive polymers. More specifically IPMCs and IP2Cs, which are electroactive polymers that can be used both as sensors and as actuators, have been considered. The modeling of IPMC and IP2C transducers, in fact, is an open issue relevant to the development of effective applications. An introductions to the general framework of the proposed models of Electroactive polymers will be given in Section 1, while, in Sections 2 and 3, a multiphysics model of actuators is presented in details. It integrates the description of the electrical, mechanical, chemical and thermal coupled physics domains in a unique solution. As a novel contribution, a model optimization procedure which integrates Nelder-Mead simplex method with the multiphysics model is exploited to identify model parameters by fitting experimental data. A further nonlinear neural network model of IP2C actuators has been implemented and the results will be described in Section 4. The proposed model takes into account the humidity dependence of the device as a modifying input. Three different Neural Network models, e.g. Feed-forward neural network, Radial-basis neural network and recurrent neural network are developed and a comparison of proposed model have been reported.