  Ionic Polymer Metal Composites (IPMCs) are one of the most preferred choices
among biocompatible materials for industrial and biomedical applications.
Despite their advantages, some of their drawbacks include non-linear and
hysteretic behavior, which complicates the modeling process. In previous works,
usually autoregressive models were used to predict the behavior of an IPMC
actuator. The main drawback of using an autoregressive model is that it cannot
be used in mobile and real-time applications. In this study, we proposed a
hybrid analytical intelligent model for an IPMC actuator. The most outstanding
feature of this model is its non-autoregressive structure. The hybrid concept
proposed in this study can be generalized to various problems other than IPMCs.
The structure used in this work comprises an analytical model and a deep neural
network, providing a non-linear, dynamic, and non-autoregressive model for the
IPMC actuator. Lastly, the average NMSE achieved using the proposed hybrid
model is 9.5781e-04 showing a significant drop in the error rate compared to
other non-autoregressive structures.
