中文摘要：

在传统子结构拟动力试验基础上，提出采用隐性卡尔曼滤波器的自适应子结构试验方法，以减小由于数值子结构中相应构件的恢复力模型误差所带来的不利影响。在子结构试验过程中，在线识别试验子结构模型参数，实时更新数值子结构中相应构件的恢复力模型参数。快速准确的恢复力模型在线识别方法成为自适应拟动力子结构试验的关键，将试验子结构恢复力模型的模型参数作为试验子结构系统状态变量的一部分，采用隐性卡尔曼滤波器在线识别其模型参数。通过数值仿真检验采用隐性卡尔曼滤波器在线识别的自适应子结构试验方法性能。结果表明：所提出的自适应子结构试验方法具有很好的精度和较快的识别速度，试验结果较传统子结构试验结果有较大改善。

英文摘要：

An adaptive substructure testing method with unscented Kalman filter on the basis of traditional substructure pseudo --dynamic testing method is proposed in order to deal with the negative influence from the inaccuracy of the restore force model of numerical substructure. An online identification technique is adopted to estimate hysteretic model parameters of the testing substructure and modify the model parameters of the numerical substructures in the substructure testing. Fast and accurate hysteretic model identification is the key issue of adaptive substructure testing. In this paper, the parameters of hysteretic mod- el of experimental substructure are taken as a part of the state vectors of experimental substructure and identified online with unscented Kalman filter. The performances of the proposed method are verified through numerical simulation. Results show that the new method has good accuracy and high computation efficiency, and test results are significantly improved over con- ventional substructure method with unchanged mathematical model of numerical substructure.