Low-order control-oriented modeling of piezoelectric actuator using Huberian function with low threshold: pseudolinear and neural network models

This paper presents a new methodology of nonlinear system identification using Huberian function. Pseudolinear and Neural network black box model families are applied to identify a piezoelectric actuator for suspensions and vibration assisted drilling. Huberian function, which is a mixture of L2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_2$$\end{document} and L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_1$$\end{document} norms with a threshold, is used to estimate a parameters vector in these model families. Pseudolinear black box model with reduced model order and balanced simplicity-accuracy neural network model families are proposed. Moreover, we show the interest to decrease the threshold in the Huberian function by providing efficient models for vibration drilling control. Experimental results are presented and discussed.