Identification of an ionic polymer metal composite actuator employing Preisach type fuzzy NARX model and Particle Swarm Optimization

Abstract

An ionic polymer metal composite (IPMC) actuator is an electro-active polymer (EAP) that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network. One drawback of IPMC in the use of sensors and actuators is the existence of strong hysteresis effects, nonlinearities, and uncertainties. The aims of this paper is the identification of a nonlinear black-box model for IPMC actuators based on a novel Preisach type fuzzy Nonlinear Auto Regressive Exogenous (NARX) model and modified Particle Swarm Optimization (PSO).Firstly, an IPMC actuator is investigated. The open-loop input voltage signals are applied to the IPMC in order to investigate the IPMC characteristics. Secondly, a proper Preisach type fuzzy NARX model is developed with one input and one output to estimate the IPMC tip displacement. By employing the collected training data, an identification scheme based on incorporation of least squares method and Particle Swarm Optimization (PSO) algorithm is then carried out to tune the model parameters. Finally, modeling results proved the ability of proposed scheme to estimate the bending behaviors of IPMC actuators.