Robust intelligent modeling for giant magnetostrictive actuators with rate‐dependent hysteresis

Abstract

Purpose

This paper proposes a robust modeling method of a giant magnetostrictive actuator which has a rate‐dependent nonlinear property.

Design/methodology/approach

It is known in statistics that the Least Wilcoxon learning method developed using Wilcoxon norm is robust against outliers. Thus, it is used in the paper to determine the consequence parameters of the fuzzy rules to reduce the sensitiveness to the outliers in the input‐output data. The proposed method partitions the input space adaptively according to the distribution of samples and the partition is irrelative to the dimension of the input data set.

Findings

The proposed modeling method can effectively construct a unique dynamic model that describes the rate‐dependent hysteresis in a given frequency range with respect to different single‐frequency and multi‐frequency input signals no matter whether there exist outliers in the training set or not. Simulation results demonstrate that the proposed method is effective and insensitive against the outliers.

Originality/value

The main contributions of this paper are: first, an intelligent modeling method is proposed to deal with the rate‐dependent hysteresis presented in the giant magnetostrictive actuator and the modeling precision can fulfill the requirement of engineering, such as the online modeling issue in the active vibration control; and second, the proposed method can handle the outliers in the input‐output data effectively.

Citation

Zhang, W. and Mao, J. (2012), "Robust intelligent modeling for giant magnetostrictive actuators with rate‐dependent hysteresis", International Journal of Intelligent Computing and Cybernetics, Vol. 5 No. 4, pp. 500-514. https://doi.org/10.1108/17563781211282259