A modified prandtl-ishlinskii model for modeling asymmetric hysteresis of piezoelectric actuators

Hong-wei, Jiang; Ji, Hongli; Qiu, Jinhao; Chen, Yuansheng

doi:10.1109/tuffc.2010.1533

Cited by 134 publications

(70 citation statements)

References 22 publications

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“…5, pp. 1200-1210, May 2010 P. Li, F. Yan, C. Ge, X. Wang, L. Xu, J. Guo, and P. Li, "A simple fuzzy system for modelling of both rate-independent and rate-dependent hysteresis in piezoelectric actuators, " Mechanical Systems and Signal Processing, vol. 36, pp.…”

Section: Referencesmentioning

confidence: 99%

“…A survey on these models can be found in [22]. Furthermore, the experimentation showed that the hysteresisnon-linearity in PEA is not symmetric and many models was proposed in [23]- [25] to describe the asymmetric hysteresis existing in PEA. To compensate the hysteresis behavior of PEA, many intelligent techniques was used such as fuzzy logic [26], [27], neural networks [28], [29], adaptive filter [30], [31], hybrid models [32], NARMAX models [33], [34] and iterative learning control [35].…”

Section: Introductionmentioning

confidence: 99%

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Identification of the PEA Hysteresis Property Using a Minimum Variance Scheme

Rebai¹,

Guesmi²,

Hemici³

2014

IJCEE

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Abstract-The minimum variance principle is, generally, used for controller's synthesis. In this paper, we propose another use of this principle to identify the hysteresis property of piezoelectric actuators using the Extended Least Squares identification technique adapted for the ARMAX model. IndexTerms-Piezoelectric actuator, nonlinearity, hysteresis, identification. I. INTRODUCTIONThe piezoelectric actuators (PEA) based on the inverse piezoelectric effect are used in many fields due to their properties. Indeed, for example they are very used in the ultra-precision applications [1]- [3]. However, the hysteresis property, existing in piezoelectric materials, makes the modeling and the control of PEA difficult. Many nonlinear models was developed in the literature to describe the hysteresis property of piezoelectric actuators such as the Preisach model and its modifications [4]-[8], the Duhem model [9], [10], the Maxwell Resistance Capacitor (MRC) model [11], the Bouc-Wen model [12]-[15], the PrandtlIshlinskii model [16]-[20] and the modified Rayleigh model [21]. A survey on these models can be found in [22]. Furthermore, the experimentation showed that the hysteresisnon-linearity in PEA is not symmetric and many models was proposed in [23]- [25] to describe the asymmetric hysteresis existing in PEA. To compensate the hysteresis behavior of PEA, many intelligent techniques was used such as fuzzy logic [26] [35]. The most previous models are nonlinear and difficult to implement in on-line which makes the controller synthesis and analysis difficult. To deal with this problem, the PEA can be described by linear models using identification algorithms [36], [37]. In this paper, we propose a technique for the description of the hysteresis property. This technique is based on the modification of the minimum variance controller algorithm to be used for identification purpose. This paper is organized as follows: the extended least squares recursive identification method is described in Section II, then, the proposed minimum variance identification scheme is presented in Section III and before concluding, the proposed approach is validated through simulation results. Manuscript received March 9, 2014; revised June 20, 2014. A. Rebai and B. Hemici are with the National Polytechnic School of Algiers, Algeria (e-mail: aissa.rebai@g.enp.edu.dz, bhemici@yahoo.fr).K. Guesmi is with the CReSTIC, Reims University, France (e-mail: guesmi01@univ-reims.fr). II. THE EXTENDED LEAST SQUARES (ELS) IDENTIFICATION METHODSThe piezoelectric actuator can be described by the ARMAX model of the following expression:And y(t), u(t) are respectively the output and the input signals, e(t) is a white noise with zero mean value and constant variance and d is pure time delay.Hence, model (1) can be written as:( 1)  The prediction error between the real and the estimated output () εt is defined by:We define also the criterion J(t): International Journal of Computer and Electrical Engineering, Vol. 6, No. 4, August 2014 DOI: 10.7763/IJCEE.20...

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Section: Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of the PEA Hysteresis Property Using a Minimum Variance Scheme

Rebai¹,

Guesmi²,

Hemici³

2014

IJCEE

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“…The research topics include modeling of static piezoelectric hysteresis, modeling of dynamic hysteresis, feedback and feed-forward control of hysteresis, and self-sensing technique in feedback control of piezoelectric hysteresis. Several models of piezoelectric hysteresis, including the model based on hyperbola functions, the modified PrandtlIshilinskii model, and the model based on neural network have been proposed [28][29][30]. In this section, the new modified Prandtl-Ishlinskii model is introduced.…”

Section: Nonlinear Hysteresis Controlmentioning

confidence: 99%

“…The proposed asymmetric play operator [29] consists of two parts, which are called the right-side play operator (RSPO) and the left-side play operator (LSPO). They respectively represent the right-side hysteretic nonlinearity and the leftside hysteretic nonlinearity of a system with hysteresis.…”

Section: The Modified Prandtl-ishlinskii Modelmentioning

confidence: 99%

Research on applications of piezoelectric materials in smart structures

Qiu

2011

Front. Mech. Eng. China

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Piezoelectric materials have become the most attractive functional materials for sensors and actuators in smart structures because they can directly convert mechanical energy to electrical energy and vise versa. They have excellent electromechanical coupling characteristics and excellent frequency response. In this article, some research activities on the applications of piezoelectric materials in smart structures, including semi-active vibration control based on synchronized switch damping using negative capacitance, energy harvesting using new electronic interfaces, structural health monitoring based on a new type of piezoelectric fibers with metal core, and active hysteresis control based on new modified Prandtl-Ishlinskii model at the Aeronautical Science Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics are introduced.

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“…A modified Prandtl-Ishlinskii model was proposed by Jiang et al (2010) based on two asymmetric hysteresis operators.…”

Section: Prandtl-ishlinskii Modelmentioning

confidence: 99%

The Application of Piezoelectric Materials in Smart Structures in China

Qiu¹,

Ji²

2010

International Journal of Aeronautical and Space Sciences

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Piezoelectric materials have become the most attractive functional materials for sensors and actuators in smart structures because they can directly convert mechanical energy to electrical energy and vise versa. They have excellent electromechanical coupling characteristics and excellent frequency response. In this article, the research activities and achievements on the applications of piezoelectric materials in smart structures in China, including vibration control, noise control, energy harvesting, structural health monitoring, and hysteresis control, are introduced. Special attention is given to the introduction of semi-active vibration suppression based on a synchronized switching technique and piezoelectric fibers with metal cores for health monitoring. Such mechanisms are relatively new and possess great potential for future applications in aerospace engineering.

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A modified prandtl-ishlinskii model for modeling asymmetric hysteresis of piezoelectric actuators

Cited by 134 publications

References 22 publications

Identification of the PEA Hysteresis Property Using a Minimum Variance Scheme

Identification of the PEA Hysteresis Property Using a Minimum Variance Scheme

Research on applications of piezoelectric materials in smart structures

The Application of Piezoelectric Materials in Smart Structures in China

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