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Modeling of Rate-Dependent Hysteresis in Piezoelectric Actuators Using a Hammerstein-Like Structure with a Modified Bouc-Wen Model

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Intelligent Robotics and Applications (ICIRA 2016)

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Abstract

This paper presents a modified Bouc-Wen (MBW) model based Hammerstein-like structure to describe the hysteresis in piezoelectric actuators (PEAs) with asymmetric and rate-dependent characteristics. Firstly, a MBW model with a third-order input function is proposed to characterize the hysteresis with asymmetric feature. Then, to describe the rate-dependent behavior of the hysteresis in PEAs, the MBW model is cascaded with a linear dynamics model as a Hammerstein-like model structure. To derive the parameters of this model structure, three identification steps are performed with different input signals: (i) the delay time of the PEA-actuated nanopositioning stage is tested with a step signal input; (ii) the linear dynamics model is identified with a low-amplitude white noise input; (iii) with the identified delay time and linear dynamics model, nonlinear least squares optimization method is adopted to derive the parameters of the MBW model using a multiple-amplitude triangular signal input with low frequency. Finally, to evaluate the Hammerstein-like model structure, experiments are carried out on a PEA-actuated nanopositioning stage. The experimental results verify that the predicted responses of the MBW model based Hammerstein-like structure well match the system responses.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China under Grant No. 51405293 and the Specialized Research Fund for the Doctoral Programme of Higher Education under Grant No. 20130073110037.

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Correspondence to Guo-Ying Gu .

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Li, CX., Li, LL., Gu, GY., Zhu, LM. (2016). Modeling of Rate-Dependent Hysteresis in Piezoelectric Actuators Using a Hammerstein-Like Structure with a Modified Bouc-Wen Model. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_58

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  • DOI: https://doi.org/10.1007/978-3-319-43506-0_58

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