Estimating Preisach Density via Subset Selection

Abstract

Preisach density is drawing increasing attention for interpreting material properties for memory and storage electronics. Preisach density can be linked to the observed hysteresis loops via the Preisach model that is based on the superposition of relay operators. Reconstructing Preisach density from hysteresis is an ill-posed problem with nonunique solutions. To alleviate ambiguities, we address Preisach density reconstruction as a constrained subset selection task utilizing structured sparsity regularizations. We validate our approach under various simulation settings and apply it on experimental band-excitation piezoresponse spectroscopy (BEPS) datasets to gain insights in microstructure-dependent properties of the tip-surface contact.

Adaptive Control of a Piezo-Positioning Mechanism With Hysteresis and Input Saturation Using Time Delay Estimation

“…There are four main methods for hysteresis modeling. The first is the physics-based modeling method, which mainly includes Jiles-Atherton model [9], Maxwell-slip model [10] and so on; The second method is based on differential equations, including Bouc-Wen model [11], Duhem model [12], and so on; The third one is based on operators, which includes Preisach model [13], Prandtl-Ishlinskii model [14], etc. The last one is intelligent modeling method based on computational intelligence, such as neural network model [15] and support vector machine model [16].…”