Modeling of hysteretic response of porous piezo/ferroelectric ceramics

Stirbu, Radu Stefan; Mitoseriu, Liliana

doi:10.1016/j.commatsci.2023.112633

Cited by 2 publications

(2 citation statements)

References 53 publications

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“…To mathematically model hysteresis behavior, several models have been developed. The Preisach model [ 23 ] represents hysteresis using a distribution of hysterons, while the Jiles–Atherton [ 24 ] model adapts concepts from magnetic materials to describe piezoelectric hysteresis. The Bouc–Wen model, commonly used in structural engineering, captures non-linear hysteresis through a set of differential equations [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control

Artetxe,

Barambones,

Calvo

et al. 2024

Micromachines

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In recent years, there has been significant interest in incorporating micro-actuators into industrial environments; this interest is driven by advancements in fabrication methods. Piezoelectric actuators (PEAs) have emerged as vital components in various applications that require precise control and manipulation of mechanical systems. These actuators play a crucial role in the micro-positioning systems utilized in nanotechnology, microscopy, and semiconductor manufacturing; they enable extremely fine movements and adjustments and contribute to vibration control systems. More specifically, they are frequently used in precision positioning systems for optical components, mirrors, and lenses, and they enhance the accuracy of laser systems, telescopes, and image stabilization devices. Despite their numerous advantages, PEAs exhibit complex dynamics characterized by phenomena such as hysteresis, which can significantly impact accuracy and performance. The characterization of these non-linearities remains a challenge for PEA modeling. Recurrent artificial neural networks (ANNs) may simplify the modeling of the hysteresis dynamics for feed-forward compensation. To address these challenges, robust control strategies such as integral fast terminal sliding mode control (IFTSMC) have been proposed. Unlike traditional fast terminal sliding mode control methods, IFTSMC includes integral action to minimize steady-state errors, improving the tracking accuracy and disturbance rejection capabilities. However, accurate modeling of the non-linear dynamics of PEAs remains a challenge. In this study, we propose an ANN-based IFTSMC controller to address this issue and to enhance the precision and reliability of PEA positioning systems. We implement and validate the proposed controller in a real-time setup and compare its performance with that of a PID controller. The results obtained from real PEA experiments demonstrate the stability of the novel control structure, as corroborated by the theoretical analysis. Furthermore, experimental validation reveals a notable reduction in error compared to the PID controller.

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Section: Introductionmentioning

confidence: 99%

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control

Artetxe,

Barambones,

Calvo

et al. 2024

Micromachines

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“…In recent work [153] the authors simulate the porosity-dependent evolution of polarization-field and strainfield hysteresis responses of porous ferro/piezoceramics within the Jiles-Atherton model for switching, combined with Finite Element Analysis. Using these techniques they determine the local electrical and mechanical responses and the effective dielectric and piezoelectric constants.…”

mentioning

confidence: 99%

The Preisach model of hysteresis: fundamentals and applications

Semenov,

Borzunov,

Meleshenko

et al. 2024

Phys. Scr.

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The Preisach model is a well-known model of hysteresis in the modern nonlinear science. This paper provides an overview of works that are focusing on the study of dynamical systems from various areas (physics, economics, biology), where the Preisach model plays a key role in the formalization of hysteresis dependencies. Here we describe the input-output relations of the classical Preisach operator, its basic properties, methods of constructing the output using the demagnetization function formalism, a generalization of the classical Preisach operator for the case of vector input-output relations. Various generalizations of the model are described here in relation to systems containing ferromagnetic and ferroelectric materials. The main attention we pay to experimental works, where the Preisach model has been used for analytic description of the experimentally observed results. Also, we describe a wide range of the technical applications of the Preisach model in such fields as energy storage devices, systems under piezoelectric effect, models of systems with long-term memory. The properties of the Preisach operator in terms of reaction to stochastic external impacts are described and a generalization of the model for the case of the stochastic threshold numbers of its elementary components is given.

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Modeling of hysteretic response of porous piezo/ferroelectric ceramics

Cited by 2 publications

References 53 publications

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control

Combined Control for a Piezoelectric Actuator Using a Feed-Forward Neural Network and Feedback Integral Fast Terminal Sliding Mode Control

The Preisach model of hysteresis: fundamentals and applications

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