MODELING AND CONTROL OF MAGNETOSTRICTIVE ACTUATORS

Most smart actuators exhibit rate-dependant hysteresis when the working frequency is higher than 5Hz. Although the Preisach model has been a very powerful tool to model the static hysteresis, it cannot be directly used to model the dynamic hysteresis. Some researchers have proposed various generalizations of the Preisach operator to model the rate-dependant hysteresis, however, most of them are application-dependant and only valid for low frequency range. In this thesis, a first-order dynamic relay operator is proposed. It is then used to build a novel dynamic Preisach model. It can be used to model general dynamic hysteresis and is valid for a large frequency range. Real experiment data of magnetostrictive actuator is used to test the proposed model. Experiments have shown that the proposed model can predict all the static major and minor loops very well and at the same time give an accurate prediction for the dynamic hysteresis loops. The controller design using the proposed model is also studied. An inversion algorithm is developed and a PID controller with inverse hysteresis compensation is proposed and tested through simulations. The results show that the PID controller with inverse compensation is good at regulating control; its tracking performance is really limited (average error is 10 micron), especially for high frequency signals. Hence, a simplified predictive control scheme is developed to improve the tracking performance. It is proved through experiments that the proposed predictive controller can reduce the average tracking error to 2 micron while preserve a good regulating performance

A simple scheme for the inversion of a Preisach like hysteresis operator in saturation conditions

A class of operators based on a Prandtl-Ishilinskii operator with inverse in a closed form is presented. Conversely to those considered in the past, they describe the B - H constitutive equation and not the usual J - H link. This allows its application in numerical schemes for the description of nonlinear dynamic circuits in transient conditions, with low formulation effort and computational weight, with respect to the standard inversion of the operator. The model has been implemented into a numerical scheme describing a RL nonlinear and hysteretic circuit, outlining the effects of residual magnetization and coercive field on the global current dynamics. The model performances are preliminary compared to numerical model based on the standard numerical inversion of the operator, along with the experimental results of transient current analysis

Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel

A computationally efficient hysteresis model, based on a standalone deep neural network, with the capability of reproducing the evolution of the magnetization under arbitrary excitations, is here presented and applied in the simulation of a commercial grain-oriented electrical steel sheet. The main novelty of the proposed approach is to embed the past history dependence, typical of hysteretic materials, in the neural net, and to illustrate an optimized training procedure. Firstly, an experimental investigation was carried out on a sample of commercial GO steel by means of an Epstein equipment, in agreement with the international standard. Then, the traditional Preisach model, identified only using three measured symmetric hysteresis loops, was exploited to generate the training set. Once the network was trained, it was validated with the reproduction of the other measured hysteresis loops and further hysteresis processes obtained by the Preisach simulations. The model implementation at a low level of abstraction shows a very high computational speed and minimal memory allocation, allowing a possible coupling with finite-element analysis (FEA)

Hybrid dynamical model for reluctance actuators including saturation, hysteresis and eddy currents

A novel hybrid dynamical model for single-coil, short-stroke reluctance actuators is presented in this paper. The model, which is partially based on the principles of magnetic equivalent circuits, includes the magnetic phenomena of hysteresis and saturation by means of the generalized Preisach model. In addition, the eddy currents induced in the iron core are also considered, and the flux fringing effect in the air is incorporated by using results from finite element simulations. An explicit solution of the dynamics without need of inverting the Preisach model is derived, and the hybrid automaton that results from combining the electromagnetic and motion equations is presented and discussed. Finally, an identification method to determine the model parameters is proposed and experimentally illustrated on a real actuator. The results are presented and the advantages of our modeling method are emphasized

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model's parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model's potential in the field of materials science

The magnetoelectric effect in gallium iron oxide

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Magneto-Mechanical Model for Hysteresis in Electrical Steel Sheet

A coupled magneto-mechanical model for hysteresis in an electrical steel sheet is presented. The foundation of the model developed is the classical Sablik-Jiles-Atherton (SJA) model. A comprehensive model for the stress dependent magnetostriction is also proposed and implemented in the SJA model. Improvements in the SJA model as well, are proposed and validated with simultaneous measurements of magnetostriction, magnetic field and flux density. The measurements were performed on a single electrical steel sheet under various levels of stress (-35 MPa to 100 MPa). The proposed model was found to adequately model the permeability change and the local bowing of the BH-loop due to stress.Peer reviewe

Control of Hysteresis in Smart Actuators, Part I: Modeling, Parameter Identification, and Inverse Control

Hysteresis in smart actuators presents a challenge in control of these actuators. A fundamental idea to cope with hysteresis is inverse compensation. In this paper we study modeling, identification and inverse control of hysteresis in smart actuators through the example of controlling a commercially available magnetostrictive actuator. The (rate-independent) Preisach operator has been used extensively to model the hysteresis in smart actuators. We present efficient inversion algorithms for the Preisach operator that are implementable in real-time. The magnetostrictive hysteresis is rate-dependent at high frequencies. For this we propose a novel dynamic hysteresis model by coupling a Preisach operator to an ordinary differential equation. This model can capture the dynamic and hysteretic behavior of the magnetostrictive actuator, and it provides insight into modeling of rate-dependent hysteresis in other smart materials. The effectiveness of the identification and inverse control schemes is demonstrated through extensive experimental results