Inverse Hysteresis Model Research Articles

Iron Loss Analysis Considering Excitation Conditions Under Alternating Magnetic Fields

In this paper, the nature of iron loss in electrical steel during alternating field excitation is investigated more precisely. The exact definition of AC iron loss is cleared by accurately measuring the iron loss for conditions of both the sinusoidal magnetic field and sinusoidal magnetic flux density. The results of this approach to iron loss calculations in electrical steel are compared to experimentally-measured losses. In addition, an inverse hysteresis model considering eddy current loss was developed to analyze the iron loss when the input is the voltage source. With this model, the inrush current in the inductor or transformer as well as the iron loss can be calculated.

Measurement and Simulation of Vector Hysteresis Characteristics

This paper presents a Preisach model of ferromagnetic hysteresis to simulate the vector hysteresis properties of ferromagnetic materials. Vector behavior has been studied using a single sheet tester with a round shaped specimen at low frequency, and the magnetic flux density vector has been controlled by a digital measurement system. Circular magnetic flux density pattern has been measured. An inverse vector Preisach hysteresis model has been developed and identified by applying the measured data. Finally, the inverse model has been inserted into a finite element procedure through the fixed point technique and the reduced magnetic scalar potential formulation. The applicability of the measurement system as well as the developed model has been proven by comparing measured and simulated results.

Feedforward Controller of Ill-Conditioned Hysteresis Using Singularity-Free Prandtl–Ishlinskii Model

Piezoelectric, magnetostrictive, and shape memory alloy actuators are gaining importance in high-frequency precision applications constrained by space. Their intrinsic hysteretic behavior makes control difficult. The Prandtl-Ishlinskii (PI) operator can model hysteresis well, albeit a major inadequacy: the inverse operator does not exist when the hysteretic curve gradient is not positive definite, i.e., ill condition occurs when slope is negative. An inevitable tradeoff between modeling accuracy and inversion stability exists. The hysteretic modeling improves with increasing number of play operators. But as the piecewise continuous interval of each operator reduces, the model tends to be ill-conditioned, especially at the turning points. Similar ill-conditioned situation arises when these actuators move heavy loads or operate at high frequency. This paper proposes an extended PI operator to map hysteresis to a domain where inversion is well behaved. The inverse weights are then evaluated to determine the inverse hysteresis model for the feedforward controller. For illustration purpose, a piezoelectric actuator is used.

Sliding mode control of the inchworm displacement with hysteresis compensation

This paper proposes a new modeling scheme to describe the hysteresis and the preload characteristics of piezoelectric stack actuators in the inchworm. From the analysis of piezoelectric stack actuator behavior, the hysteresis can be described by the functions of a maximum input voltage and the preload. The dynamic characteristics are also identified by the frequency domain modeling technique based on the experimental data. The hysteresis is compensated by the inverse hysteresis model for precise control of inchworm displacement. Since the dynamic stiffness of an inchworm is generally low compared to its driving condition, the mechanical vibration may degrade accuracy of the inchworm. Therefore, the SMC (Sliding Mode Control) and the Kalman filter are developed for the motion control of the inchworm. The feasibility of the proposed modeling scheme and the control algorithm is tested and verified experimentally.

Micropositioning using shape memory alloy actuators

Shape memory alloys (SMA) can be used to generate motion or force in electromechanical devices and micro-machines, although their accuracy is severely limited by their highly nonlinear and hysteretical stimulus-response characteristics. In this work we present some results regarding a nonlinear control method suitable for SMA-based positioning applications. In particular, we show how the hysteresis effects can be compensated using an inverse hysteresis model generated by a neural network, trained using experimental data. The control strategy, experimented on a laboratory SMA actuator, uses the inverse model inserted in a proportional-integral with antiwindup control loop. It is found that neural networks successfully improve the closed-loop response, leading to position accuracies close to a micrometer.

Neural network-based micropositioning control of smart shape memory alloy actuators

Shape memory alloys (SMA) are a special kind of smart materials whose dimensions change because of a temperature-dependent structural phase transition. This property can be used to generate motion or force in electromechanical devices and micromachines. However, their highly nonlinear hysteretical stimulus–response characteristic fundamentally limits the accuracy of SMA actuators. The purpose of this work is to design nonlinear control methods suitable for SMA-based positioning applications. To account for the hysteresis effects, inverse hysteresis models are inserted in proportional integral with antiwindup control loops. The inverse hysteresis models are obtained both using a linear phase shift approximation and by training neural networks using experimental data. It is found that neural networks are excellent tools perfectly capable of learning the hysteresis effects. Several control strategies, with and without compensation, are experimented on a laboratory SMA actuator and it is found that neural networks successfully improve the closed-loop response, leading to position accuracies close to the micron.

NEURAL MODELING AND CONTROL OF DYNAMIC SYSTEMS WITH HYSTERESIS

A hysteretic operator is proposed to set up an expanded input space so as to transform the multi-valued mapping of hysteresis to a one-to-one mapping so that the neural networks can be applied to model of the behavior of hysteresis. Based on the proposed neural modeling strategy for hysteresis, a pseudo control scheme is developed to handle the control of nonlinear dynamic systems with hysteresis. A neural estimator is constructed to predict the system residual so that it avoids constructing the inverse model of hysteresis. Thus, the control strategy can be used for the case where the output of hysteresis is unmeasurable directly. Then, the corresponding adaptive control strategy is presented. The application of the novel modeling approach to hysteresis in a piezoelectric actuator is illustrated. Then a numerical example of using the proposed control strategy for a nonlinear system with hysteresis is presented.

Intelligence rules of hysteresis in the feedforward trajectory control of piezoelectrically-driven nanostagers

Piezoelectrically-driven nanostagers have limited performance in a variety of feedforward and feedback positioning applications because of their nonlinear hysteretic response to input voltage. The hysteresis phenomenon is well known for its complex and multi-path behavior. To realize the underlying physics of this phenomenon and to develop an efficient compensation strategy, the intelligence properties of hysteresis with the effects of non-local memories are discussed here. Through performing a set of experiments on a piezoelectrically-driven nanostager with a high resolution capacitive position sensor, it is shown that for the precise prediction of the hysteresis path, certain memory units are required to store the previous hysteresis trajectory data. Based on the experimental observations, a constitutive memory-based mathematical modeling framework is developed and trained for the precise prediction of the hysteresis path for arbitrarily assigned input profiles. Using the inverse hysteresis model, a feedforward control strategy is then developed and implemented on the nanostager to compensate for the ever-present nonlinearity. Experimental results demonstrate that the controller remarkably eliminates the nonlinear effect, if memory units are sufficiently chosen for the inverse model.

Position Controller for Piezoelectric Actuator

This paper presents a computer-based tracking control approach for a piezoelectric actuator based on incorporating a feedforward loop with a PID feedback controller. The purpose of this paper is to improve the hysteresis characteristics of a stack type piezoelectric actuator using the hysteresis nonlinearity compensator. The system proposed in this study prints by spraying the molten metal, and consists of a nozzle, heating furnace, operating actuator, and an XYZ 3-axis stage. As an operating system, the piezoelectric(PZT) method has very valuable uses. The PZT actuator, however, has a hysteresis nonlinearity due to the ferroelectric characteristics of the PZT element. This causes problems in the system position control characteristics and deteriorates the performance of the system. This study proposed a inverse hysteresis model, a mathematic modeling method that can express the geometric relationship between voltage and displacement, in order to reduce the hysteresis of the PZT actuator. In addition, system identification and PID control methods were examined. Also, it was confirmed that the proposed control strategy gives good tracking performance.

Hysteresis nonlinearity compensator for piezoelectric actuator

The purpose of this paper is to improve the hysteresis characteristics of a stack type piezoelectric actuator using the hysteresis nonlinearity compensator. Recently, several printing methods that cost less and are faster than previous semiconductor processes have been developed for the production of electric paper and RFID. The system proposed in this study prints by spraying the molten metal, and consists of a nozzle, heating furnace, operating actuator, and an XYZ 3-axis stage. As an operating system, the piezoelectric (PZT) method has very valuable uses. However, the PZT actuator has a hysteresis nonlinearity due to the ferroelectric characteristics of the PZT element. This causes problems in the system position control characteristics and deteriorates the performance of the system. This study proposed an inverse hysteresis model, a mathematic modeling method that can express the geometric relationship between voltage and displacement, in order to reduce the hysteresis of the PZT actuator. In addition, system identification and PID control methods were examined. Also, it was confirmed that the proposed control strategy gives good tracking performance.

Hysteresis compensation for giant magnetostrictive actuators using dynamic recurrent neural network

According to the hysteresis characteristics of the giant magnetostrictive actuator (MA), a dynamic recurrent neural network (DRNN) is constructed as the inverse hysteresis model of the MA, and an on-line hysteresis compensation control strategy combining the DRNN inverse compensator and a proportional derivative (PD) controller is used for precision position tracking of the MA. Simulation results validate the excellent performances of the proposed strategy.

An adaptive inverse method of control for a piezoelectric actuator

Hysteresis hinders the wider applicability of piezoelectric actuators. In this paper, amathematical model is proposed for describing hysteresis precisely. On the basis of thehysteresis model, an adaptive inverse control approach is presented, for reducing hysteresis.The weights of the inverse hysteresis model are identified by using a least-mean-squaresalgorithm. The realization of an adaptive inverse controller for the linearization of apiezoelectric actuator is formulated. Experiments were performed on a micropositioningsystem driven by piezoelectric actuators. The experimental results demonstrate that themaximum linearity error caused by hysteresis is lowered by about one order of magnitude.

이송자벌레를 위한 압전소자의 모델링 및 운동제어 : 2. 슬라이딩 모드법에 의한 이송자벌레의 운동제어

This paper presents an algorithm for the precision motion control based on the dynamic characteristics of piezoelectric actuators in the inchworm. The dynamic characteristics are identified by the frequency domain modeling technique using the experimental data. For the motion control, the hysteresis behavior is compensated by the inverse hysteresis model. The dynamic stiffness of an inchworm is generally low compared to its driving condition, so mechanical vibration may degenerate the motion accuracy of the inchworm. The Sliding mode controller and the Kalman filter are designed for motion control of the inch-worm.

The application of initial state correction in iterative learning control and the experimental validation on a piezoelectric tube scanner

In iterative learning control (ILC) it is generally assumed that the initial state is the same at the start of each iteration. In various applications, this assumption is easily violated. In this paper an initial state correction procedure is proposed to take into account the effect of a varying initial state. The algorithm is experimentally validated on a piezoelectric tube scanner. In these experiments, the piezoelectric tube scanner is linearized by means of a Preisach inverse hysteresis model.

The application of initial state correction in iterative learning control and the experimental validation on a piezoelectric tube scanner

In iterative learning control (ILC) it is generally assumed that the initial state is the same at the start of each iteration. In various applications, this assumption is easily violated. In this paper an initial state correction procedure is proposed to take into account the effect of a varying initial state. The algorithm is experimentally validated on a piezoelectric tube scanner. In these experiments, the piezoelectric tube scanner is linearized by means of a Preisach inverse hysteresis model.

The inverse jiles-atherton model parameters identification

An optimization procedure to obtain the parameters of both the original Jiles-Atherton and the inverse Jiles-Atherton hysteresis models is presented in this paper. Doing so, minor loops can be conveniently simulated without inserting scaling methods. The comparison between experimental and calculation results validates the procedure.

역히스테리시스 모델과 PID-신경회로망 제어기를 이용한 압전구동기의 정밀 위치제어

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is an inverse hysteresis model, base on neural network and the feedback control is implemented with PID control. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance.

Fast Preisach-based magnetization model and fast inverse hysteresis model

Fast computational methods for Preisach-based models and their inverses are presented. The methods are based on a differential equation approach to computing a sequence of magnetization values due to a sequence of applied fields. The method used to speed up calculations can be applied to any Preisach model. The Della Torre, Oti, Kadar model was used here as an illustration. Sequential computations for the magnetization model are substantially faster than for standard Preisach models with comparable output error. Computations for the inverse hysteresis model are even faster than for the model. Using the inverse, open loop control of magnetic hysteresis is simulated, showing hysteretic material tracking a desired magnetization in a linear manner for both major loops and minor loops, with less than 2% error in inversion for the waveforms used. The effect of misidentification of material parameters on operation of the inverse is also investigated through simulations.