Dynamic model identification of IPMC actuator using fuzzy NARX model optimized by MPSO

Anh Pham Huy Ho,Nam Thanh Nguyen

doi:10.32508/stdj.v17i1.1295

Abstract

In this paper, a novel inverse dynamic fuzzy NARX model is used for modeling and identifying the IPMC-based actuator’s inverse dynamic model. The contact force variation and highly nonlinear cross effect of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experiment 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 dynamic fuzzy NARX model trained by MPSO algorithm yields outstanding performance and perfect accuracy.

Highlights

The nonlinear IPMC-based actuator is belonged characteristics of Ionic Polymer Metal Composite (IPMC) [1,2,3,4]
In order to overcome this disadvantage, 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
- 2π π/512 on the experimental input-output data values measured from the IPMC actuator system

Summary

INTRODUCTION

The nonlinear IPMC-based actuator is belonged characteristics of Ionic Polymer Metal Composite (IPMC) [1,2,3,4]. In order to overcome this disadvantage, 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 MPSO algorithm optimally generates the appropriate fuzzy if- rules to perfectly characterize the dynamic features of the IPMC actuator system These good results are due to proposed IFN model combines the extraordinary approximating capability of the fuzzy system with the powerful predictive and adaptive potentiality of the nonlinear NARX structure that is implied in the proposed IFN model. The proposed MPSO-based IPMC inverse fuzzy NARX model identification approach has successfully modeled the nonlinear dynamic IPMC system with better performance other identification methods. Both of these methods have the same fuzzy inference structure (FIS)

MPSO-based IPMC IFN Model Identification

EXPERIMENT CONFIGURATION OF THE IPMC IFN MODEL IDENTIFICATION

PSO ALGORITHM FOR NARX FUZZY MODEL IDENTIFICATION

IDENTIFICATION RESULTS

CONCLUSIONS