Preisach modeling of hysteresis for piezoceramic actuator system

Yu, Yunhe; Naganathan, Nagi G.; Dukkipati, Rao V.

doi:10.1016/s0094-114x(01)00065-9

Cited by 116 publications

(52 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the rate-dependent Preisach hysteresis models, the Preisach weighting function  ,     are adjusted to account for the input-rate dependency by using rate-dependent multiplicative modifying factors [68,70] or neural network approaches [69]. In [68], the rate-dependent multiplicative modifying factor for adjusting   MME is a function of 1) the average voltage input rate between two input extrema and 2) the variation of the voltage between two input extrema.…”

Section: Rate-dependent Preisach and Pi Hysteresis Modelsmentioning

confidence: 99%

“…Even though this model can effectively represent the PEA hysteresis up to 800 Hz, it requires the a priori knowledge of the input voltage waveform (at least to the next extremum), and thus being not applicable to conventional situations in which the future input voltage is unknown. To avoid this problem, in [70] a multiplicative modifying factor which depends only on the input-rate or the first derivative of the input voltage is used instead. However, the small differences between the input-rate at the input extrema at different input frequencies limits the model accuracy at higher input frequencies (the model is effective typically below 10 Hz).…”

Section: Rate-dependent Preisach and Pi Hysteresis Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

View full text Add to dashboard Cite

Piezoelectric actuators (PEAs) have been widely used in micro-and nanopositioning applications due to their fine resolution, fast responses, and large actuating forces. However, the existence of nonlinearities such as hysteresis makes modeling and control of PEAs challenging. This paper reviews the recent achievements in modeling and control of piezoelectric actuators. Specifically, various methods for modeling linear and nonlinear behaviors of PEAs, including vibration dynamics, hysteresis, and creep, are examined; and the issues involved are identified. In the control of PEAs as applied to positioning, a review of various control schemes of both model-based and non-model-based is presented along with their limitations. The challenges associated with the control problem are also discussed. This paper is concluded with the emerging issues identified in modeling and control of PEAs for future research.

show abstract

Section: Rate-dependent Preisach and Pi Hysteresis Modelsmentioning

confidence: 99%

Section: Rate-dependent Preisach and Pi Hysteresis Modelsmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

View full text Add to dashboard Cite

show abstract

“…Tan et al [27] updated the Preisach operator with the recursive output-based identification method. However, Deng et al commented that some dynamic phenomenological hysteresis models [28,29] which reconstructed the weights functions are difficult for online controls [30]. Specially, introducing a dynamic threshold…”

Section: Introductionmentioning

confidence: 99%

Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys

Mai

Song

Liao

2015

Neural Comput & Applic

View full text Add to dashboard Cite

Shape-memory alloys (SMAs) have received considerable amount of attentions for their engineering applications in recent years. The hysteresis in SMAs is sensitive to the state-varying tendency and frequency. Utilizing past information to estimate the hysteretic behavior gets increasing attention. In this paper, a timedelayed dynamic neural network (TDDNN) is proposed for modeling hysteresis of SMAs in online applications. By introducing a time delay between the input and output response, the TDDNN considers the time delay's effect on the hysteresis. This proposed network was applied to a SMA wire actuator. Experimental results demonstrate the effectiveness of TDDNN. The identified results obtained by TDDNN are better than those obtained by dynamic neural network without considering the delay information. It demonstrates the importance of introducing the time delay. The different values of time delay item can also affect TDDNN's identified results.

show abstract

“…Research works (Mayergoyz 1991;Ge and Jouaneh 1995;Freeman and Joshi 1995;Hughes and Wen 1995;Yu et al 2002) had been conducted to prescribe hysteresis effects of piezoelectric structures via casting the lumped input-output relation of piezoelectric device/system into a pre-chosen mathematical models, such as Preisach (Mayergoyz 1991;Preisach 1935;Hutton 2009), BoucWen (Andronikou et al 1983), Prandtl-Ishlinksii (Dong and Tan 2009;Janaideh et al 2009) or domain wall (Chen and Wang 1998). These approaches of ''macroscopic'' modeling on the hysteresis effects requires experimental identification on the parameters of the hysteresis model adopted, whenever a new piezo-structure is considered.…”

Section: Introductionmentioning

confidence: 99%

Robust control design for precision positioning of a generic piezoelectric system with consideration of microscopic hysteresis effects

et al. 2011

View full text Add to dashboard Cite

This study performs precision positioning of a generic piezoelectric structure against hysteresis effects by finite elements, microscopic hysteresis cancellation and robust H ? compensation. The designed control algorithm is expected to be effective in enhancing servo performance of hard disk drives. The precision positioning is accomplished by adding a polarization term into the linear constitutive equations of piezoelectric materials. This polarization term is then described by the well-known Preisach model. Applying basic principles of finite elements and Hamilton's thoery, the macroscopic governing equations of an arbitrary piezoelectric system in finite elements are obtained. Based on the macro-model, a controller consisting of two parts is designed to perform the precision positioning of a generic piezo-structure. The first part is responsible for direct hysteresis cancellation at the microscopic level, while the second one is a robust H ? controller to overcome inevitable cancellation errors. In this way, the control effort is then more effective than the conventional PI and double-lead controller without microscopic hysteresis cancellation. A simple piezoelectric structure of a bender-bimorph cantilever beam is considered for designs and experimental validation. Based on experimental results, the proposed control design is found effective to suppress hysteresis effects as opposed to conventional controllers.

show abstract

Preisach modeling of hysteresis for piezoceramic actuator system

Cited by 116 publications

References 11 publications

A Survey of Modeling and Control of Piezoelectric Actuators

A Survey of Modeling and Control of Piezoelectric Actuators

Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys

Robust control design for precision positioning of a generic piezoelectric system with consideration of microscopic hysteresis effects

Contact Info

Product

Resources

About