Particle swarm optimization identification of IPMC actuator using fuzzy NARX model

Abstract

In this paper, a novel inverse fuzzy NARX model is used for modeling and identifying the IPMC-based actuator's inverse dynamic model. The highly nonlinear features of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experimental input-output training data. This paper proposes the novel use of a modified particle swarm optimization (MPSO) to generate the inverse fuzzy NARX (IFN) model for a highly nonlinear IPMC actuator system. The results show that the novel inverse fuzzy NARX model optimized by MPSO yields outstanding performance and perfect accuracy.