Abstract
Smart material actuators like Piezoelectric(PZT) are widely used in Micro/Nano manipulators, but their hysteresis behaviors are complex and difficult to model. Most hysteresis models are based on elementary quasistatic operators and are not suitable for modeling rate-dependent or thermal-drift behaviors of the actuators. This work proposes a Preisach model based neurodynamic optimization model to account for the complex hysteresis behaviors of the smart material actuator system. Through simulation study, the rate-dependent and the thermal-drift behaviors are simulated via Bouc-Wen model. The μ-density function of the Preisach model is identified on-line through neurodynamic optimization method to suit for the varied rate of the input signals. The output of the actuator system is predicated in realtime based on the on-line identified μ-density plane. It is shown experimentally that the predicated hysteresis loops match the simulated PZT loops very well.
This work was supported in part by National Natural Science Foundation of China (Grant No. 61128008), Macao Science and Technology Development Fund (Grant No. 016/2008/A1), and Research Committee of University of Macau (Grant No. MYRG203(Y1-L4)-FST11-LYM).
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Xiao, S., Li, Y. (2012). Modeling Rate-Dependent and Thermal-Drift Hysteresis through Preisach Model and Neural Network Optimization Approach. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_21
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DOI: https://doi.org/10.1007/978-3-642-31346-2_21
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