基于改进粒子滤波算法的Bouc-Wen模型参数在线识别方法

在线模型参数更新是提高结构混合试验中数值子结构模型精度的有效手段。为了提高强非线性模型参数在线识别精度,在标准粒子滤波算法的基础上提出了一种改进的辅助无迹粒子滤波算法。在重要性采样中,基于最新观测信息采用无迹卡尔曼滤波方法计算每一个粒子估计,以提高粒子非线性变换估计精度;在重采样过程中,引入辅助因子修正粒子权值,以丰富粒子多样性、削弱粒子退化现象。采用改进粒子滤波算法针对Bouc-Wen模型进行了在线参数识别,并与标准粒子滤波算法、扩展卡尔曼粒子滤波算法以及无迹粒子滤波算法的参数识别精度和计算效率进行对比分析。结果表明,与其它3种算法相比,辅助无迹粒子滤波算法在单步计算耗时增加的基础上,在线参数识别精度明显提高,参数识别值波动幅度显著降低。最后,通过橡胶隔震支座拟静力试验,验证了采用改进粒子滤波算法在线识别Bouc-Wen模型参数方法的有效性。

Online parameters identification method of Bouc-Wen model based on modified particle algorithm

Online model parameter updating is an effective method to improve the model accuracy of numerical substructure in the hybrid simulation. In order to improve the precision of online parameters identification for the nonlinear model, an improved auxiliary unscented particle filter (AUPF) was proposed in this paper base on the standard particle filter algorithm. In the stage of the importance sampling, the unscented Kalman filter (UKF) method was adopted to calculate estimates of all particles using the latest observation information in order to improve estimation precision of the nonlinear transformation of the particles. In the stage of the resampling, an auxiliary factor was introduced to modify particle weights, which enriched the particle diversity and weaken the particle degradation phenomenon. Parameters online identification for the Bouc-Wen were conducted with the AUPF, and both the identification precision and the calculation efficiency of results were compared with the standard particle filter algorithm (PF), The rextended Kalman particle filter algorithm (EPF), and unscented particle filter algorithm (UPF). The results show that compared with the other three algorithms, the proposed AUPF algorithm can effectively improve the precision of online parameters identification of the Bouc-Wen model and reduce the fluctuation of parameters identification values on the basis of increasing computing time-consuming in a single step. Finally, the effectiveness of the Bouc-Wen model parameter identification method using the AUPF algorithm was verified through the quasi-static test of the rubber isolation bearing.