Preisach Model Research Articles

Device modeling of oxide–semiconductor channel antiferroelectric FETs using half-loop hysteresis for memory operation

In this work, the memory characteristics of oxide–semiconductor (OS) channel antiferroelectric FETs (AFeFETs) are investigated by developing a compact model of AFeFETs. The model consists of an analytic junctionless FeFET model and a newly developed AFe Preisach model. The AFe Preisach model can reproduce arbitrary minor loop measurement results. The key feature of AFeFETs is using the half-loop hysteresis of AFe in contrast to the full hysteresis of Fe. Only a small net charge is required and minority carrier generation is not necessary for erase operation, which is preferable for a typical OS channel. Based on the developed model, we systematically vary the device parameters of the OS channel and AFe gate insulator, analyze the memory window using operation point analysis, and show the potential of OS channel AFeFETs for memory applications.

Training, Control and Application of SMA-Based Actuators with Two-Way Shape Memory Effect

Shape memory alloys (SMAs) are widely used in aerospace, automobile, and other fields because of their excellent properties, such as large driving force and large deformation. A training method with a bidirectional memory effect is proposed for SMA actuators. The trained SMA units can be heated and cooled to change their shape (shorten and extend). The trained SMA is used as an actuator to drive the deformation of a structure. Due to the obvious hysteresis characteristics of SMA, a temperature-displacement hysteresis model based on the Preisach model is proposed in order to reduce the influence of hysteresis in the process of structural deformation. The F function method (FFM) is used for Preisach numerical implementation, and a PID control method is used for the precise control of structural deformation. Compared with the PID control method without hysteresis model, this method is superior to the PID control method in response speed and control accuracy. The maximum relative error of three target points in the experiment is 5.45%, which is better than the PID control method without this model. The hysteresis model can be applied to the displacement control of a SMA-based actuator.

A case history of modeling elastic hysteresis of different-genotype rocks based on the Preisach model

A case history of modeling elastic hysteresis of different-genotype rocks based on the Preisach model

Magnetic loss in grain-oriented Fe–Si sheets under different harmonic excitation and high indication

This paper presents an improved loss calculation method that takes into account the effects of harmonics and skinning effects, thus extending the loss prediction of soft magnetic materials to a larger frequency and induction range. First, the first-order and second-order slew curves are concurrently fitted into a nonlinear Preisach model to determine the hysteresis loss. Then, integrating the effects of high frequency harmonics and high induction, the fixed-point technique based on the H–B magnetic intrinsic relationship is introduced into the finite element calculation in order to calculate the flux density distribution of the silicon steel sheet more accurately. Then, the classical loss at high flux density is further calculated more accurately. Finally, losses for soft magnetic materials in the 1 kHz range are computed under various harmonic excitations. This method is compared to the traditional method and the experimental results, and this method is closer to the experimental results.

A hybrid dynamic hysteresis model based on the Preisach model and stacked auto-encoder

With the increase in power electronic equipment in power system, the excitation of ferromagnetic materials often involves a large number of harmonics. Therefore, it is necessary to construct an accurate dynamic hysteresis model to adapt to this complicated operating state of electrical equipment. In this paper, a Hybrid Dynamic Hysteresis Model (HDHM), which can effectively characterize the harmonic excitation of materials is studied based on the Preisach model and Stacked Auto-Encoder (SAE) model. The static part of this model takes the form of the inverse Preisach model. And the Multiple Dynamic Hysteresis Model Set (MDHMS) is constructed by multiple dynamic models of eddy currents and excess characteristics of the ferromagnetic materials. The dynamic part of the HDHM takes the form of the model structure combining the Stacked Auto-encoder and the MDHMS. The calculation results of the hysteresis loop and ferromagnetic loss in the harmonic condition of silicon steel sheet proves the validity of this model. Moreover, compared with the conventional dynamic hysteresis model, the HDHM has better accuracy and generalization ability.

Моделирование и управление хаотическим процессом

The article investigates problems related to the control of the dynamics of a system given by the Henon map with a hysteresis component included in it. In particular, possible modifications of the limit set (attractor) of the modified Henon map under hysteresis conditions are studied. The hysteresis element is formalized based on design approach by means of the Preisach model, which is approximated by a system consisting of a finite set of non-ideal relays. To analyze the dynamics, numerical simulation is carried out for various values of the model parameters, which are characterized by chaotic dynamics. For this purpose, a Python script has been developed that simulates the dynamics of the system under hysteresis conditions, and also processes the results to identify dynamic modes. Based on the data obtained, a comparative analysis of strange attractors of the modified and classical Henot mappings is carried out. Next, we study the dynamics depending on the parameters of the modified Henon map. To detect various dynamic regimes, bifurcation diagrams were plotted, the high Lyapunov exponent was calculated based on the Rosenstein algorithm and the 0-1 test was produced depending on the system parameters, and the hysteresis nonlinearity parameter. Established, that hysteresis term regularize dynamics of the system compared to the classical map and changed in the position of bifurcation points in the space of system parameters.

Ferroelectric FDSOI FET modeling for memory and logic applications

In this paper, we present a Verilog-A based compact model for simulating Ferroelectric Fully Depleted Silicon-on-Insulator (Fe-FDSOI) FET. The model can capture the rich physics of ferroelectric (FE) materials and reproduce the important electrical characteristics, such as history effect, the impact of threshold voltage on pulse width and amplitude as well as potentiation–depression characteristics. The FE switching is modeled using Preisach model to capture the Polarization (P)–Voltage (V) characteristics. In addition, we capture the history-dependent minor loop characteristics to obtain multiple states of polarization. This allows the modeling of multiple conductance states, which forms the fundamental prerequisite for neuromorphic applications as well as multi-level non-volatile memories. The underlying baseline FDSOI FET is modeled using the industry-standard BSIM-IMG compact model. The model is then augmented with the physics-based model of FE capacitor to realize Fe-FDSOI FET. Our model is computationally efficient and carefully calibrated to reproduce experimental measurement data.

Diagonal Recurrent Neural Network-Based Hysteresis Modeling.

The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with the binary step activation function. For the first time, the hysteresis nature and conditions of the classical dRNN with the tanh activation function are mathematically discovered and investigated, instead of using the common black-box approach and its variants. It is shown that the dRNN neuron is a versatile rate-dependent hysteresis system under specific conditions. The dRNN composed of those neurons can be used for modeling the rate-dependent hysteresis and it can approximate the Preisach model with arbitrary precision with specific parameters for rate-independent hysteresis modeling. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.

A benchmark problem for the verification of numerical calculation methods considering magnetic hysteresis

Previously, 36 TEAM benchmark problems have been established, used to verify the numerical calculation methods of electromagnetic fields. Among them, there is only one problem related to the magnetic field considering magnetic hysteresis, which is problem 32. The results of it are obtained by experimental measurement. In this paper, a same kind of benchmark problem is proposed, in which the field results are obtained by using analytical method. This benchmark problem can be used to verify both 2D and 3D numerical calculation methods. The analytical solving process of the problem is that H is obtained by using Ampere’s law, then B is calculated based on the hysteresis model of the material, so that the field distribution results are formed. The required hysteresis model could be any kind, such as Preisach model, J–A model, etc. As long as the same kind of model is used both in the benchmark problem and in the numerical method, the numerical method can be verified through the proposed benchmark problem. Considering the form of J–A hysteresis model required by the benchmark problem does not exist, which is the field separation-based forward dynamic J–A model, it is derived based on the forward static J–A model.

Modeling of stator sheets punching effect on EV motor performances—SMM25:374178

The effect of punching of electric motor sheets on electrical machine performances is addressed. The approach is using first measurement of mechanical stress effect on magnetic behavior. An evaluation of residual stress field in stator sheets after punching process is then carried out by FEM. A new coupled magneto-elastic Preisach model is proposed to model the B(H) curves and introduced in magnetic FEM software using residual stresses as an input. Modeling of EV motor highlights a weak torque reduction.

A Dynamic Model for Prediction of Hysteresis and Loss Property of Soft Material Under Multiple Harmonic Magnetizations

This article presents an improved algorithm for identification of the excess loss statistical parameter to get more accurate prediction of multiple harmonic magnetic hysteresis and loss properties of grain-oriented silicon steel sheets. Magnetic hysteresis characteristics of a specimen are measured on a magnetic measurement platform. Static hysteresis property can be simulated accurately by the Preisach model. The excess loss field expression under multiple harmonic magnetizations is constructed based on an extraction algorithm for the statistical parameters of excess loss. According to the equivalence of loss separation and field separation, a new dynamic model is established. In the case of different multiple harmonic magnetizations, the magnetic hysteresis characteristics obtained by predictions are compared with the test results. Also, the influences of peaked flux density, harmonic order, and harmonic content upon the total iron loss are investigated.

Recurrent-Neural-Network-Based Rate-Dependent Hysteresis Modeling and Feedforward Torque Control of the Magnetorheological Clutch

The complicated rate-dependent hysteresis due to the input current and rotation speed of the magnetorheological clutch (MRC) greatly hinders its applications in robotics. In this article, it is first proved that the Preisach model is a special diagonal recurrent neural network (dRNN) with a binary step activation function. Motivated by this, the condition and the internal knowledge of the classical dRNN in modeling the rate-dependent hysteresis are investigated and validated. To guarantee that such a condition holds in dRNN training, a new loss function is proposed. The coupled rate-dependent hysteresis of an MRC prototype is, then, precisely modeled by the dRNN with the current input and speed input. By employing the trained dRNN model, the MRC torque is controlled using the feedforward scheme with no external torque sensors. Taking the advantage of being free-of-parameter tuning for the classical inverse multiplicative structure control (IMSC), a dRNN-based IMSC is proposed by finding the best feasible neuron and solving a nonlinear function. Various experiments validate much better control accuracy and higher bandwidth of the IMSC over the model-based proportional–integral–derivative control.

Hysteresis dynamics of rare earth nickelates: unusual scaling exponent and asymmetric spinodal decomposition

Understanding the dynamics of phase-transitions, interpretations of their experimental observations and their agreement with theoretical predictions continue to be a long-standing research interest. Here, we present detailed phase-transition dynamics of rare earth nickelates associated with its first-order metal–insulator transition. The thermal hysteresis shows absence of training effect and defies the Preisach model. A large phase-coexistence in insulating state during cooling suggests kinetically arrested glassy dynamics of the phase-transition. Experimentally derived hysteresis scaling exponent is much larger than the mean-field predicted universal value of 2/3. In the phase-coexistence region, the quench and hold measurement depicts higher stability of the metallic state compare to that of the insulating one; highlighting the manifestation of phase-coexistence via asymmetric spinodal decomposition. All these observations for nickelates are in stark contrast to the phase-transition dynamics of canonically similar vanadates but are closer to those of glasses, alloys. A substantial disagreement between the experiment and theory emphasizes the necessity to incorporate system-dependent details for the accurate interpretation of the experimental results.

Application of Least-Squares Support-Vector Machine Based on Hysteresis Operators and Particle Swarm Optimization for Modeling and Control of Hysteresis in Piezoelectric Actuators

Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy.

Calculation Method of Core Loss Under Intermediate Frequency Excitation

Based on Brockhaus magnetic measurement system, the magnetic properties of GT100 ultra-thin silicon steel under middle and high frequency sinusoidal excitation are measured. Based on the Preisach model, the static hysteresis and loss characteristics are simulated accurately. In addition, an improved loss model was proposed. The formula of eddy current loss was improved according to skin effect principle, and the statistical parameter V0 of abnormal loss was adjusted by iterative algorithm. The simulation results of sinusoidal dynamic hysteresis loop and iron loss at different frequencies and different flux densities are compared with the measured results, which verifies the accuracy and versatility of the improved model. In addition, the influence of increasing frequency and flux density on total loss is also studied.

On the Modeling of Polycrystalline Ferroelectric Thin Films: Landau-Based Models Versus Monte Carlo-Based Models Versus Experiment

Due to the potential for technological application, there has been an explosion of interest in heavily polycrystalline ferroelectric nanofilms, such as those of doped hafnium oxide. However, the heavily polycrystalline nature of these materials invalidates conventional modeling approaches as the dynamics have been found to be: 1) nucleation-limited; 2) involve grains of ferroelectric material interspersed among grains of alternative, nonferroelectric material; and 3) the direct interaction between these grains is observed to be minimal. In this article, we consider seven separate compact or “0-D” models of such polycrystalline films. Four of these models are based on a Landau paradigm and two are based on a Monte Carlo (MC) paradigm. The seventh is the traditional Preisach model. Although all of these models have been used in the literature to model novel polycrystalline ferroelectric nanofilms, here we compare and contrast the accuracy and physical appropriateness of each model by comparing both their static and dynamic properties against experimental data. We then find that although all models except single-grain models are capable of reproducing the static properties, only the MC models replicate the long-time dynamical properties. Thus, it is demonstrated that not all models are equally valid for the accurate modeling of such films.

Kalman filter and classical Preisach hysteresis model applied to the state of charge battery estimation

The goal of this work is first to include a hysteresis model in the classical equivalent circuit model (ECM) for a battery system and then to improve the estimation of the state of charge (SoC) by applying the Extended Kalman Filter (EKF). The hysteretic behavior of the open circuit voltage (OCV) is modelled with the classical Preisach model used for magnetic materials. The construction of the Preisach operator is made by means of the Everett function identified from experimental data which only involve the charging curves of the battery. Thus, a significant reduction in the time necessary to obtain the measurements is achieved. The model is assessed with some laboratory experiments performed on a lithium-ion battery and the results show that with this procedure hysteresis is very well reproduced, even when interior loops are present. In addition, the use of the EKF allows us to eliminate the measurements noise and ensure the accuracy of SoC estimation. The high computational efficiency and precision of the method, joined to the limited computational resources needed for the numerical implementation, make it particularly suitable for real-time embedded battery management system (BMS) applications. In addition, the proposed methodology is well-adapted to any battery type, independently of the SoC-OCV profile of the hysteresis cycle.

Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with Hysteresis.

Future improvements in particle accelerator performance are predicated on increasingly accurate online modeling of accelerators. Hysteresis effects in magnetic, mechanical, and material components of accelerators are often neglected in online accelerator models used to inform control algorithms, even though reproducibility errors from systems exhibiting hysteresis are not negligible in high precision accelerators. In this Letter, we combine the classical Preisach model of hysteresis with machine learning techniques to efficiently create nonparametric, high-fidelity models of arbitrary systems exhibiting hysteresis. We experimentally demonstrate how these methods can be used insitu, where a hysteresis model of an accelerator magnet is combined with a Bayesian statistical model of the beam response, allowing characterization of magnetic hysteresis solely from beam-based measurements. Finally, we explore how using these joint hysteresis-Bayesian statistical models allows us to overcome optimization performance limitations that arise when hysteresis effects are ignored.

Classical Preisach Model Based on Polynomial Approximation and Applied to Micro-Piezoelectric Actuators

In engineering applications, where we demand more and more precision, the modeling of systems with hysteretic nonlinearity has received considerable attention. The classical Preisach model (CPM) is currently the most popular for characterizing systems with hysteresis, and this model can represent the hysteresis with an infinite but countable first-order inversion curve (FORC). The table method is a method used to realize CPM in practice. The data in the table corresponds to a limited number of FORC samples. There are two problems with this approach: First, in order to reflect the timing effects of elements with hysteresis, it needs to consume a lot of memory space to obtain accurate data table. Second, it is difficult to come up with an efficient way to modify the data table to reflect the timing effects of elements with hysteresis. To overcome these shortcomings, this paper proposes to use a set of polynomials instead of the table method to implement the CPM. The proposed method only needs to store a small number of polynomial coefficients, and thus it reduces the required memory usage. In addition, to obtain polynomial coefficients, we can use least squares approximation or adaptive identification algorithms, which can track hysteresis model parameters. We developed an adaptive algorithm for the identification of polynomial coefficients of micro-piezoelectric actuators by applying the least mean method, which not only reduces the required memory size compared to the table method implementation, but also achieves a significantly improved model accuracy, and the proposed method was successfully verified for displacement prediction and tracking control of micro-piezoelectric actuators.

A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time

Hysteresis is one of the main factors affecting the measurement accuracy of TMR sensors, especially in dynamic measurements. The commonly used Preisach hysteresis compensation model has some problems, such as complex modeling and difficulty in accurately measuring the step time, resulting in low accuracy in dynamic measurements. In this paper, considering the distribution characteristics of the conversion time of the hysteresis operator in dynamic measurements, a dynamic hysteresis model based on the probability estimation of the hysteresis operator and its conversion time is proposed. Compared with the existing methods, this method only needs to calculate the distribution of the sensor hysteresis operator to realize the calculation of hysteresis characteristics without a physical model or fitting algorithm. It has good generalization performance and a corresponding fast speed. In the test of two typical TMR sensors, compared with the transmission Preisach model, the maximum error of this method is reduced by 46.7%, the variance can be reduced by 90.2%, and the average value can be reduced by 65.1%.