Development of Reduced Preisach Model Using Discrete Empirical Interpolation Method

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.

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Preisach Density via Subset Selection

Kim

Neumayer

et al. 2020

“…Different models have been presented in the literature to describe the hysteresis phenomenon. For PZT actuators, phenomenological models are widely used, including the Preisach [5]- [8], Maxwell-slip [9], [10], Prandtl-Ishlinskii (PI) [11]- [13], and Krasnosel'skii-Pokrovskii (KP) [14]. Hysteresis compensation is complicated in these models due to the difficulty they present in deriving the inverse hysteresis model.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Ahmed

Yan

2021

A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-toone mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification (PSO) based methods, where simulation and experimental results demonstrate significant performance improvement with the proposed method.

“…Meanwhile, the feedback technique can also be applied to the compensated system to enhance the performance [8], [11]- [13]. The Preisach [14]- [16] and Prandtl-Ishlinskii models [17]- [20] are widely used to model hysteresis in piezoelectric materials. These models belong to the class of operators with a Preisach memory and are essentially a mathematical description of observed hysteresis.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Temperature-Dependent Hysteresis in Piezoelectric Materials Considering Parameter Sensitivity

Liu

2020

Self Cite

Hysteresis is an important nonlinearity effect in piezoelectric materials and is sensitive to temperature. This paper extends the saturated capacitor model to capture temperature-dependent hysteresis by replacing the constant parameters with functions of temperature. To obtain these functions, several sets of constant parameters are identified at different temperatures, and then each term of the parameters is fitted as a function with respect to temperature. In the curve fitting process, the parameter sensitivity is employed as a weight factor. The effectiveness of the proposed modeling and parameter determination procedure is verified with experimental data. The overall normalized root mean square error is approximately 5%, including 3% error caused by the identified parameters. INDEX TERMS Temperature-dependent hysteresis, modeling and identification, piezoelectric materials, saturated capacitor hysteresis model.