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Hysteresis online identification approach for smart material actuators with different input signals and external disturbances

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Abstract

In this article, we present a fraction-order polynomial-modified Prandtl–Ishlinskii (FPMPI) and online infinite impulse response (OIIR) integrated model to describe the rate/load/temperature hystereses for smart material actuators. Hysteresis loops exhibit dynamic, asymmetric and saturate phenomena under different input rates, external loads and surrounding temperatures. It is difficult to apply a comprehensive model to accurately capture the hysteresis variation. The Hammerstein-based hysteresis offline identification study is conducted. It is found that the rate/load/temperature-dependent hysteresis cannot be described well via the offline approach, due to the system uncertainty and time-varying dynamics. By formulating the hysteresis modeling as an adaptive filtering problem, the OIIR filter and FPMPI integrated model is utilized for the rate/load/temperature-dependent hysteresis identification. Comparison of the online and offline identification results shows that the hysteresis online identification accuracies improve at least one order of magnitude, and two orders for some cases.

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Acknowledgements

The work is supported by Shanghai Sailing Program (20YF1417400), Postdoctoral Science Foundation of China (2021M692022), National Nature Science Foundation of China Fund (61973207), Shanghai Rising-Star Program (20QA1403900), National Nature Science Foundation of Shanghai Fund (21ZR1423000) and the State Key Laboratory of MCMS-NUAA Fund (MCMS-E-0320G01), for which the authors are most grateful. Thanks Profs. Bintang Yang and Limin Zhu a lot for supplying the magnetostrictive and piezoelectric devices, respectively. The authors declare that we have no conflict of interest and comply with ethical standards. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

    Sicheng Yi, Quan Zhang, Liang Xu, Tianhong Wang & Long Li

  2. Artificial Intelligence Institute, Shanghai University, Shanghai, China

    Sicheng Yi & Quan Zhang

  3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Sicheng Yi

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  1. Sicheng Yi
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Correspondence to Quan Zhang.

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Yi, S., Zhang, Q., Xu, L. et al. Hysteresis online identification approach for smart material actuators with different input signals and external disturbances. Nonlinear Dyn 110, 2557–2572 (2022). https://doi.org/10.1007/s11071-022-07677-z

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