|
|
| |
| 基于改进中心差分卡尔曼算法的滞回模型参数识别 |
| Identification of Hysteresis Model Parameters Based on Improved Central Difference Kalman Filter Algorithm |
| 投稿时间：2024-10-27 修订日期：2025-01-06 |
| DOI： |
| 中文关键词: Bouc-Wen滞回模型 中心差分卡尔曼算法 QR分解 参数识别 |
| 英文关键词:Bouc-Wen Hysteresis Model Central Differential Kalman Algorithm QR Decomposition Parameter Identification |
| 基金项目: |
|
| 摘要点击次数: 2 |
| 全文下载次数: 0 |
| 中文摘要: |
| 在动荷载作用下土木工程结构会表现出显著的非线性滞回特性,其系统状态和模型参数的识别对确定模型捏缩效应及强度和刚度退化至关重要。中心差分卡尔曼滤波算法(CDKF)算法能够较好地进行系统状态和模型参数的识别,传统的CDKF算法通过乔里斯基分解(Cholesky decomposition)来求解协方差矩阵的平方根,必须确保协方差矩阵为正定,可能导致递推过程中断。为此本文提出了一种改进中心差分卡尔曼算法,该方法使用QR分解替代Cholupdate,克服了协方差矩阵必须正定严格要求,使递归计算过程无条件数学稳定。对不同噪声条件下的Bouc-wen模型系统进行数值计算,结果表明,改进的中心差分卡尔曼滤波算法能准确识别滞回模型的系统状态和模型参数,并有较强的稳定性和抗噪性。 |
| 英文摘要: |
| Under the action of dynamic loads, civil engineering structures exhibit significant nonlinear hysteresis characteristics, and the identification of their system states and model parameters is crucial for determining the model"s pinching effect and strength and stiffness degradation. The Center Difference Kalman Filter (CDKF) algorithm can effectively identify system states and model parameters. Traditional CDKF algorithms use Cholesky decomposition to solve the square root of the covariance matrix, which must ensure that the covariance matrix is positive definite, which may cause interruption in the recursive process. This article proposes an improved center difference Kalman algorithm, which uses QR decomposition instead of Cholupdate to overcome the strict requirement that the covariance matrix must be positive definite, making the recursive calculation process unconditionally mathematically stable. Numerical calculations were performed on the Bouc wen model system under different noise conditions, and the results showed that the improved center difference Kalman filter algorithm can accurately identify the system state and model parameters of the hysteresis model, and has strong stability and noise resistance. |
| 查看/发表评论 下载PDF阅读器 |
|
|
|
|
