Modeling hysteresis in piezoelectric actuators using NARMAX models

Deng, Liang; Tan, Yonghong

doi:10.1016/j.sna.2008.09.022

Cited by 60 publications

(19 citation statements)

References 18 publications

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“…In [85], a non-linear auto-regressive moving average model with exogenous inputs (NARMAX) was employed to represent the hysteresis of a PEA. In [73] a hysteresis model was developed for PEAs by using a polynomialbased linear mapping strategy.…”

Section: Other Hysteresis Modelsmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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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.

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Section: Other Hysteresis Modelsmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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“…For example, Ref. [58] used an empirical operator to connect with the NARMAX model, with a model fit error of less than 0.1% finally obtained.…”

Section: Other Modelsmentioning

confidence: 99%

A Survey of Modeling and Control Issues for Piezo-electric Actuators

Cao

Chen

2014

Journal of Dynamic Systems, Measurement, and Control

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Piezo-electric actuators (PEAs) have been widely used in nanopo sitioning applications due to their high stiffness, fast responses, and large actuating forces. However, the existence o f nonlinear ities such as hysteresis can greatly deteriorate their performance and, as such, modeling and control o f PEAs fo r improved per formance has drawn considerable attention in the literature. This paper presents a brief survey o f recent achievements in modeling and control o f PEAs as well as the relevant issues that remain to be resolved. Specifically, various methods fo r modeling hysteresis, creep, and vibration dynamics in PEAs are examined, followed by a discussion o f the issues leading to modeling errors. Recently reported PEA control schemes are surveyed along with their advantages and disadvantages. The challenges associated with control problems are also discussed.

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“…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

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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.

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Modeling hysteresis in piezoelectric actuators using NARMAX models

Cited by 60 publications

References 18 publications

A Survey of Modeling and Control of Piezoelectric Actuators

A Survey of Modeling and Control of Piezoelectric Actuators

A Survey of Modeling and Control Issues for Piezo-electric Actuators

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

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