压电执行器的Bouc-Wen模型在线参数辨识

现有的定参数Bouc-Wen模型由于无法表征压电执行器迟滞具有的频移和时变性,极易产生较大的模拟误差。为了精确地模拟压电执行器的迟滞特性,本文建立了压电执行器的Bouc-Wen模型,并采用递推最小二乘在线辨识方法来实时辨识Bouc-Wen模型的参数。为了避免出现数据饱和现象,使用限定记忆来限定辨识方法所使用的数据组数。为验证该辨识方法的有效性,建立了相应的实验系统对其进行实验验证。实验结果表明,限定记忆递推最小二乘在线辨识方法能使Bouc-Wen模型也呈现频移和时变特性。以100 Hz的驱动电压为例,其最大绝对模拟误差从1.38μm降为0.51μm。因此,与传统的离线参数辨识方法相比,限定记忆递推最小二乘在线辨识方法能够有效地提高Bouc-Wen模型的模拟精度。

Online parameter identification of Bouc-Wen model for piezoelectric actuators

The exciting Bouc-Wen model with fixed-parameters can not characterize the frequency-dependent and time-varying properties from the hysteresis of piezoelectric actuators and easy to generate simulation errors. In order to accurately describe these characteristics, the Bouc-Wen model was established and a recursive least square online identification method was proposed to identify the parameters of the Bouc-Wen model in real-time. Meanwhile, the limited memory method was used to limit the data sets to avoid the data saturation phenomenon. To verify the availability of the identification method, an experimental system was set up and the performance of the identification method was experimentally verified. Experimental results show that the limited memory recursive least square identification method makes the Bouc-Wen model show the frequency shift and time-varying characteristics. When the drive voltage is set to be 100 Hz, the largest absolute simulation error decreases from 1.38 μm to 0.51 μm, and reduced by 63.7%. Compared with the traditional off line parameter identification, the online identification effectively improves the modeling accuracy of the Bouc-Wen model.