Identification of a nonlinear black-box model for a self-sensing polymer metal composite actuator

An ion polymer metal composite (IPMC) is an electro-active polymer that bends in response to a small applied electrical field as a result of the mobility of cations in the polymer network and vice versa. The aim of this paper is the identification of a novel accurate nonlinear black-box model (NBBM) for IPMC actuators with self-sensing behavior based on a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM). Firstly, an IPMC actuator is investigated. Driving voltage signals are applied to the IPMC in order to identify the IPMC characteristics. Secondly, the advanced NBBM for the IPMC is built with suitable inputs and output to estimate the IPMC tip displacement. Finally, the model parameters are optimized by the collected input/output training data. Modeling results show that the proposed self-sensing methodology based on the optimized NBBM model can well describe the bending behavior of the IPMC actuator corresponding to its applied power without using any measuring sensor