Application of nonlinear neural network model for self sensing characteristic in an Ionic Polymer Metal Composite (IPMC) actuator

Abstract

This paper focuses on a self sensing characteristic of an Ionic Polymer Metal Composite (IPMC) bases on a novel accurate nonlinear black-box model (NBBM) to estimate the IPMC tip displacement. The NBBM is formed by a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM). The model parameters are optimized by using a set of training data. The ability of NBBM model is evaluated by a comparison of the estimated and real IPMC bending characteristic. © 2011 Springer-Verlag.