First order reversal curves （FORC） of nanocomposite Nd2Fe14B/Fe3B magnetic materials were measured to attain a FORC diagram, which characterizes reversible magnetization, irreversible magnetization, and magnetic int...First order reversal curves （FORC） of nanocomposite Nd2Fe14B/Fe3B magnetic materials were measured to attain a FORC diagram, which characterizes reversible magnetization, irreversible magnetization, and magnetic interactions in a hysteresis system. Then, generalized mov- ing Preisach model （GMPM） was implemented based on the FORC diagram. Reversible and irreversible magnetizations shown in FORCs and a FORC diagram were used as an input of GMPM. Coupling interaction between reversible and irreversible magnetizations was added when calculating reversible magnetization. Meanwhile, irreversible magnetic moments＇ interaction was approximately represented by mean field interaction. The result shows that the simulated main curves mostly coincide with the experimental curves.展开更多
Preisach model is widely used in modeling of smart materials. Although first order reversal curves （FORCs） have often found applications in the fields of physics and geology, they are able to serve to identify Preis...Preisach model is widely used in modeling of smart materials. Although first order reversal curves （FORCs） have often found applications in the fields of physics and geology, they are able to serve to identify Preisach model. In order to clarify the relationship between the Preisach model and the first order reversal curves, this paper is directed towards： （1） giving the reason a first order reversal curve is introduced; （2） presenting, for identifying Preisach model, two discrete methods, which are analytically based on first order reversal curves. Herein also is indicated the solution＇s uniqueness of these two identifying methods. At last, the validity of these two methods is verified by simulating a real smart actuator both methods have been applied to.展开更多
The conception of aircraft morphing wings thrives in aeronautics since the appearance of shape memory alloys(SMAs). An aircraft morphing wing device, manipulated by an SMA actuator, inherits the intrinsic nonlinear hy...The conception of aircraft morphing wings thrives in aeronautics since the appearance of shape memory alloys(SMAs). An aircraft morphing wing device, manipulated by an SMA actuator, inherits the intrinsic nonlinear hysteresis from the SMA actuator, ending up with control disadvantages. Conventionally, systems with SMA actuators are constrained to bi-stable states to bypass the hysteresis region. Rather than retreating a morphing wing device to bi-stable states, this paper is dedicated to transcend the morphing wing device beyond the customary limit. A methodology of discrete Preisach modeling, which identifies the hysteresis of the morphing wing device, is proposed herein. An array of discrete equal-distance points is applied to the Preisach plane in order to derive the Preisach density over the partitioned unit of the Preisach plane. Discrete Preisach modeling is fulfilled by the discrete first-order reversible curve(DFORC). By utilizing the discrete Preisach model, the aircraft morphing wing device is simulated; the validity and accuracy of discrete Preisach modeling are demonstrated by contrasting the simulated outcome with experimental data of the major hysteretic loop and the wingspan-wise displacement over time; a comparison between simulation and experimental results exhibits consistency. Afterwards, a hysteresis compensation strategy put forward in this paper is implemented for quasi-linear control of the aircraft morphing wing device, which manifests a compensated shrinking hysteresis loop and attains the initiative of extending the morphing range to the intrinsic hysteretic region.展开更多
An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of...An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.展开更多
文摘First order reversal curves （FORC） of nanocomposite Nd2Fe14B/Fe3B magnetic materials were measured to attain a FORC diagram, which characterizes reversible magnetization, irreversible magnetization, and magnetic interactions in a hysteresis system. Then, generalized mov- ing Preisach model （GMPM） was implemented based on the FORC diagram. Reversible and irreversible magnetizations shown in FORCs and a FORC diagram were used as an input of GMPM. Coupling interaction between reversible and irreversible magnetizations was added when calculating reversible magnetization. Meanwhile, irreversible magnetic moments＇ interaction was approximately represented by mean field interaction. The result shows that the simulated main curves mostly coincide with the experimental curves.
基金National Natural Science Foundation of China (50674005)
文摘Preisach model is widely used in modeling of smart materials. Although first order reversal curves （FORCs） have often found applications in the fields of physics and geology, they are able to serve to identify Preisach model. In order to clarify the relationship between the Preisach model and the first order reversal curves, this paper is directed towards： （1） giving the reason a first order reversal curve is introduced; （2） presenting, for identifying Preisach model, two discrete methods, which are analytically based on first order reversal curves. Herein also is indicated the solution＇s uniqueness of these two identifying methods. At last, the validity of these two methods is verified by simulating a real smart actuator both methods have been applied to.
基金financial supports from the National Natural Science Foundation of China (Nos. 11872207 and 50911140286)Aeronautical Science Foundation of China (No. 20162852033)+1 种基金Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX17_0248)China Scholarship Council (CSC, No. 201706830087)
文摘The conception of aircraft morphing wings thrives in aeronautics since the appearance of shape memory alloys(SMAs). An aircraft morphing wing device, manipulated by an SMA actuator, inherits the intrinsic nonlinear hysteresis from the SMA actuator, ending up with control disadvantages. Conventionally, systems with SMA actuators are constrained to bi-stable states to bypass the hysteresis region. Rather than retreating a morphing wing device to bi-stable states, this paper is dedicated to transcend the morphing wing device beyond the customary limit. A methodology of discrete Preisach modeling, which identifies the hysteresis of the morphing wing device, is proposed herein. An array of discrete equal-distance points is applied to the Preisach plane in order to derive the Preisach density over the partitioned unit of the Preisach plane. Discrete Preisach modeling is fulfilled by the discrete first-order reversible curve(DFORC). By utilizing the discrete Preisach model, the aircraft morphing wing device is simulated; the validity and accuracy of discrete Preisach modeling are demonstrated by contrasting the simulated outcome with experimental data of the major hysteretic loop and the wingspan-wise displacement over time; a comparison between simulation and experimental results exhibits consistency. Afterwards, a hysteresis compensation strategy put forward in this paper is implemented for quasi-linear control of the aircraft morphing wing device, which manifests a compensated shrinking hysteresis loop and attains the initiative of extending the morphing range to the intrinsic hysteretic region.
基金This work was partially supported by National Science Foundation of China(No.50265001)Guangxi Science Foundation(No.0339068).
文摘An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.
