基于强跟踪容积卡尔曼滤波与幂级数多项式的多自由度结构非线性行为识别

IDENTIFICATION FOR MDOF NONLINEAR STRUCTURE UPON STRONG TRACKING CUBATURE KALMAN FILTER AND POWER SERIES POLYNOMIAL MODEL

  • 摘要: 运用幂级数多项式作为结构恢复力的一种非参数化模型表征,提出一种基于强跟踪容积卡尔曼滤波(Strong Tracking Cubature Kalman Filter, STCKF)的迭代算法,利用多自由度结构的部分加速度响应测量,识别结构质量、刚度、阻尼系数及非线性恢复力。以一个含不同数量和不同模型的磁流变阻尼器的非线性结构为对象,通过数值模拟验证了该方法的有效性。通过将以上识别结果与基于改进容积卡尔曼滤波 (Updated Cubature Kalman Filter, UCKF) 的识别算法结果进行比较,表明该文方法的识别结果具有较高准确性。

     

    Abstract: Taking a power series polynomial model (PSPM) as a general nonparametric model of structural nonlinear restoring force (NRF), an iterative algorithm with strong tracking cubature Kalman Filter (STCKF) is proposed to identify structural mass, stiffness, damping parameters, and NRF of a nonlinear multi-degree-of-freedom (MDOF) structure, with the direct use of acceleration response measurement at limited degrees of freedom (DOFs). The effectiveness of the method proposed is numerically verified by a nonlinear MDOF structure involving different number of magnetorheological (MR) dampers with various parametric models for the purpose of mimicking various nonlinearity. The comparison is carried out between the results from the proposed approach and then using Updated Cubature Kalman Filter (UCKF), and higher identification accuracy of the method proposed is illustrated.

     

/

返回文章
返回