A Modified Duhem Model for Rate-Dependent Hysteresis Behaviors

Gan, Jinqiang; Mei, Zhan‐Dong; Chen, Xiaoli; Zhou, Y. M.; Ge, Ming‐Feng

doi:10.3390/mi10100680

Cited by 28 publications

(20 citation statements)

References 30 publications

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“…Base Dependency method Preisach [140] No Operator Krasnosel'skii-Pokrovskii (KP) [138] No Operator Prandtl-Ishlinskii (PI) [138] No Operator Kuhnen [141] Yes Operator Bouc-Wen [142] No Differential Power-Law [143], Duhem [144] Yes Differential approach that models hysteresis with a so-called hysteresis operator [140], [138], [141]. A simple form of such operators can be thought of a two-position relay, as the one found in the Preisach model [140].…”

Section: Ratementioning

confidence: 99%

A Review of Extrusion-Based 3D Printing for the Fabrication of Electro- and Biomechanical Sensors

et al. 2021

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In this review paper, we focus on the 3D printing technologies that consist of the extruding of fluid material in lines to form structures for electro-and biomechanical applications. Our paper reviews various 3D print technologies, materials, sensing technologies and applications of extrusion-based 3D printing. We also discuss how to overcome some of the challenges with 3D printed sensors, such as the anisotropy of the conductors as well as the drift and nonlinearity of the materials.

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Section: Ratementioning

confidence: 99%

A Review of Extrusion-Based 3D Printing for the Fabrication of Electro- and Biomechanical Sensors

et al. 2021

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“…Several differential equation-based models, such as the Bouc–Wen model [ 5 ] and the Duhem model [ 6 ], as well as operator-based models, such as the Preisach model [ 7 ], the Prandtl–Ishlinskii (PI) model [ 8 ], the Krasnosel–skiiPokrovskii (KP) model [ 9 ] and the Jiles–Atherton model [ 10 ], have modeled and characterized the hysteresis behavior of PEAs. The Duhem model is widely used to characterize the hysteresis nonlinearity in PEAs owing to its differential nature and ability to characterize the hysteresis-memory effect [ 11 ]. However, for nonlinear hysteresis systems such as PAEs, determining the parameters of the Duhem model is a challenging task, which limits its application.…”

Section: Introductionmentioning

confidence: 99%

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Ahmed

Yan

Li³

2021

Micromachines

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This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

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“…Li and his team proposed and improved a variety of models based on a fuzzy system for hysteresis and the control of piezoelectric actuators and have made remarkable achievements in the parameter identification algorithm and feedforward control [ 9 , 10 ]. Gan et al proposed a modified Duhem model to describe the rate-dependent hysteresis behaviors at high-frequency and high-amplitude excitations by combining trigonometric functions and derivatives of input signals based on the classical Duhem model [ 11 ]. Zhang et al designed a dynamic operator by improving a Prandtl–Ishlinskii(PI) model operator, making the new model closely related to the input rate so that it had the ability to characterize the hysteresis dynamic characteristics [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Inverse Compensation of Cross-Coupling Hysteresis in Piezoceramics under Multi-Input

et al. 2021

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In the fast tool servo (FTS) system for microstructure surface cutting, the dynamic voltage hysteresis of piezoelectric actuators (PEAs) and the cutting force produced in the manufacturing affect the driving accuracy and the cutting performance. For a multi-input-single-output (MISO) cutting system, in this paper, a dynamic hysteresis model based on a rate-dependent Prandtl–Ishlinskii model is proposed. A backpropagation neural network (BPNN) is established to describe the cross-coupling effect between the applied voltage and external load. An inverse dynamic model is developed to compensate the nonlinearity of PEAs. The accuracy of the model and its inverse is discussed and the performance of the inverse feedforward compensator is validated through experiments.

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A Modified Duhem Model for Rate-Dependent Hysteresis Behaviors

Cited by 28 publications

References 30 publications

A Review of Extrusion-Based 3D Printing for the Fabrication of Electro- and Biomechanical Sensors

A Review of Extrusion-Based 3D Printing for the Fabrication of Electro- and Biomechanical Sensors

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Modeling and Inverse Compensation of Cross-Coupling Hysteresis in Piezoceramics under Multi-Input

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