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Hysteresis characterization and identification of the normalized Bouc-Wen model

机译:归一化Bouc-Wen模型的磁滞特性和识别

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摘要

By normalizing the internal hysteresis variable and eliminating the redundant parameter, the normalized Bouc-Wen model is considered to be an improved and more reasonable form of the Bouc-Wen model. In order to facilitate application and further research of the normalized Bouc-Wen model, some key aspects of the model need to be uncovered. In this paper, hysteresis characterization of the normalized Bouc-Wen model is first studied with respect to the model parameters, which reveals the influence of each model parameter to the shape of the hysteresis loops. The parameter identification scheme is then proposed based on an improved genetic algorithm (IGA), and verified by experimental test data. It is proved that the proposed method can be an efficacious tool for identification of the model parameters by matching the reconstructed hysteresis loops with the target hysteresis loops. Meanwhile, the IGA is shown to outperform the standard GA. Finally, a simplified identification method is proposed based on parameter sensitivity, which indicates that the efficiency of the identification process can be greatly enhanced while maintaining comparable accuracy if the low-sensitivity parameters are reasonably restricted to narrower ranges.
机译:通过归一化内部磁滞变量并消除冗余参数,归一化的Bouc-Wen模型被认为是Bouc-Wen模型的一种改进且更合理的形式。为了促进标准化Bouc-Wen模型的应用和进一步研究,需要揭示该模型的一些关键方面。在本文中,首先针对模型参数研究归一化Bouc-Wen模型的磁滞特性,这揭示了每个模型参数对磁滞回线形状的影响。然后基于改进的遗传算法(IGA)提出了参数识别方案,并通过实验测试数据进行了验证。实践证明,通过将重构的磁滞回线与目标磁滞回线进行匹配,该方法可以作为模型参数识别的有效工具。同时,IGA表现优于标准GA。最后,提出了一种基于参数灵敏度的简化识别方法,该方法表明,如果将低灵敏度参数合理地限制在较窄的范围内,则可以在保持可比较精度的同时大大提高识别过程的效率。

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