Using neural networks in the identification of Preisach-type hysteresis models

Adly, A.A.; Abd-El-Hafiz, Salwa K.

doi:10.1109/20.668057

Cited by 102 publications

(39 citation statements)

References 8 publications

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“…The model built is as follows: 6 , which is the input of the model,ŷ i = y(i+1) is the output of the model. From 5000 samples measured in the experiment, it can obtain 4997 training samples (u i ,ŷ i ), i = 3, 4, · · · , 4999.…”

Section: Fuzzy Tree Model Of Giant Magnetostrictive Actuatormentioning

confidence: 99%

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

Mao

Ding²

2009

Sci. China Ser. F-Inf. Sci.

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A new modeling approach for nonlinear systems with rate-dependent hysteresis is proposed. The approach is used for the modeling of the giant magnetostrictive actuator, which has the rate-dependent nonlinear property. The models built are simpler than the existed approaches. Compared with the experiment result, the model built can well describe the hysteresis nonlinear of the actuator for input signals with complex frequency. An adaptive direct inverse control approach is proposed based on the fuzzy tree model and inverse learning and special learning that are used in neural network broadly. In this approach, the inverse model of the plant is identified to be the initial controller firstly. Then, the inverse model is connected with the plant in series and the linear parameters of the controller are adjusted using the least mean square algorithm by on-line manner. The direct inverse control approach based on the fuzzy tree model is applied on the tracing control of the actuator by simulation. The simulation results show the correctness of the approach.nonlinear systems with rate-dependent hysteresis, intelligent modeling and control, fuzzy tree model, T-S fuzzy model, adaptive inverse control Nonlinear systems with hysteresis generally exists in engineering, such as civil engineering, power engineering, control engineering, etc. For example, if there exists elastic-plastic components or dry friction, then relationship between the force and the displacement or the stress and the strain will be not inverse any longer. Thus the hysteresis curve is formed [1] . If the shape of the hysteresis curve varies with the frequency of the input signal, then the class of systems is called nonlinear systems with rate-dependent hysteresis. The active vibration control systems based on giant magnetostrictive actuators is a type of nonlinear systems with ratedependent hysteresis. The rate-dependent hysteresis of the magnetostrictive actuator is an essential nonlinear component, which cannot be eliminated, and it will affect the control accuracy and stability of the systems [2] .There are three classes of approaches for the modeling of hysteresis. One is the physical modeling theory, which is represented by Jiles-Atherton model. The other is the operator modeling theory,

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Section: Fuzzy Tree Model Of Giant Magnetostrictive Actuatormentioning

confidence: 99%

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

Mao

Ding²

2009

Sci. China Ser. F-Inf. Sci.

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“…Neural networks would be one of the recommended alternatives to model this residual. However, (,,) f xvu % involves the characteristic of www.intechopen.com hysteresis, the traditional nonlinear identification methods such as neural modeling technique usually cannot be directly applied to the modeling of it since the hysteresis is a non-linearity with multi-valued mapping (Adly & Abd-El-Hafiz, 1998). In Section 4, we will present a method to construct the neural estimator for (,,) f xvu % to compensate for the effect of hysteresis.…”

Section: Control Architecture For Sandwich System With Hysteresismentioning

confidence: 99%

Adaptive Control of Dynamic Systems with Sandwiched Hysteresis Based on Neural Estimator

Tan¹,

Dong²,

Zhao³

2009

Adaptive Control

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“…Besides several well-known modeling approaches, such as Preisach model [1], PI model [2], which is an important subclass of Preisach model, JA model [3], and Duhem model [1], many neural networks hysteresis models have been proposed in recent years. Adly [4] proposed the static neural networks hysteresis model by using the coordinative transformation, but it causes a bigger error for the higher order curve. Serpico and Visone [5] developed the back propagation neural networks hysteresis model, in which state vectors based on the play operator are taken as input signals.…”

Section: Introductionmentioning

confidence: 99%

Neural networks dynamic hysteresis model for piezoceramic actuator based on hysteresis operator of first-order differential equation

Dang

Tan

2005

Physica B: Condensed Matter

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Using neural networks in the identification of Preisach-type hysteresis models

Cited by 102 publications

References 8 publications

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

Intelligent modeling and control for nonlinear systems with rate-dependent hysteresis

Adaptive Control of Dynamic Systems with Sandwiched Hysteresis Based on Neural Estimator

Neural networks dynamic hysteresis model for piezoceramic actuator based on hysteresis operator of first-order differential equation

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