A new neural-network-based scalar hysteresis model

Kuczmann, Miklós; Iványi, Antal

doi:10.1109/20.996221

Cited by 33 publications

(30 citation statements)

References 6 publications

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“…Due to its ability of universal function approximation, NN has been successfully used to design controllers for several kinds of unknown nonlinearities. However, few researches have been carried out by using NN to tackle hysteresis [21][22][23]. In [21,22], a NN model is used to describe the hysteresis behavior in different frequencies with the knowledge of some properties of magnetic materials, such as loss separation property to allow the separate treatment of quasi-static and dynamic hysteresis effects.…”

Section: Introductionmentioning

confidence: 99%

“…However, few researches have been carried out by using NN to tackle hysteresis [21][22][23]. In [21,22], a NN model is used to describe the hysteresis behavior in different frequencies with the knowledge of some properties of magnetic materials, such as loss separation property to allow the separate treatment of quasi-static and dynamic hysteresis effects. In [23], a modified Luenberger observer and a NN are used to identify a general model of hysteresis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Based Adaptive Control of Piezoelectric Actuator with Unknown Hysteresis

Yan

Xie

et al. 2007

2007 American Control Conference

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This paper proposes a neural network (NN) based adaptive control of piezoelectric actuator with unknown hysteresis. Based on the classical Duhem model described by a differential equation, we explore the explicit solution to the equation and construct a new hysteretic model with a linear model in series with a piecewise continuous nonlinear function. A NN-based dynamic pre-inversion compensator is designed to cancel the effect of the hysteresis. With the incorporation of the pre-inversion compensator, an adaptive control scheme is proposed to make the position of the piezoelectric actuator track the desired trajectory. The paper has three distinct features. First, it applies NN to approximate complicate piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, no off-line training is required for the NN. Third, the stability of overall controlled piezoelectric actuator is guaranteed. Simulation results for a practical system illustrate the validation and effectiveness of the proposed method in this paper.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Based Adaptive Control of Piezoelectric Actuator with Unknown Hysteresis

Yan

Xie

et al. 2007

2007 American Control Conference

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“…Accurate Identification of Hysteresis non linearity is a crucial task for many applications such as hysteresis nonlinearity control [1], control of Shape Memory Alloy (SMA) actuators [2][3] and Piezo actuators [4][5][6], Performance Evaluation of electromagnetic devices [7].…”

Section: Introductionmentioning

confidence: 99%

Hysteresis nonlinearity identification by using RBF neural network approach

Firouzi

Shouraki

Zakerzadeh

2010

2010 18th Iranian Conference on Electrical Engineering

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In systems with hysteresis behavior like magnetic cores, Piezo actuators, Shape Memory Alloy(SMA), we essentially need an accurate modeling of hysteresis either for design or performance evaluation; also in some control applications accurate system identification is needed. One of the famous methods of Hysteresis modeling is Preisach model. In this numerical method hysteresis is modeled by linear combination of smaller hysteresis loops as an elemental operator and local memory. In this paper we discuss those Radial Base artificial neural networks (RBF) which provides natural settings in accordance with the Preisach model. It is shown that the proposed approach can represent hysteresis modeling accurately in compare with classical Preisach model and can be used for many applications such as hysteresis nonlinearity control, hysteresis identification and realization for performance evaluation and system design. For evaluation we use measured experimental data from hysteresis SMA wire as an actuator.

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“…The original vector model introduced in [53] was subsequently generalized by defining a new type of projection for the applied field vector on each direction corresponding with one scalar model [56]. Models based on neural networks have also been introduced more recently (see for example [57]). A very comprehensive review of past and present modeling techniques may be found in [58].…”

Section: G Materials Modelingmentioning

confidence: 99%