输入未知条件下基于自适应广义卡尔曼滤波的结构损伤识别

发展一种输入未知条件下的自适应广义卡尔曼滤波(Adaptive Extended Kalman Filter with Unknown Inputs,AEKF-UI)方法,在线复合反演系统参数与未知输入,结合基于改进粒子群优化算法的自适应技术实现系统时变参数追踪,进而识别结构损伤,包括损伤发生的时间、位置和程度。建立基础隔震结构实验模型及理论模型,其中隔震层的非线性动力学特性通过Bouc-Wen模型描述。对基础隔震结构进行振动实验研究,采用刚度元件装置模拟时间、位置和程度不同的结构损伤,基于测得的加速度响应和AEKF-UI方法进行实时系统参数与未知输入的同步反演。研究结果表明:在两种典型地震波激励下,AEKF-UI方法得到的识别值与参考值相一致,验证了该方法在系统辨识中的有效性和准确性。

Structural damage identification using adaptive extended kalman filter with unknown inputs

In order to effectively monitor structure state under the circumstance of system input unavailable, an adaptive extended kalman filter with unknown inputs approach, referred to as AEKF-UI, is developed to identify the structural parameters, such as the stiffness, damping and nonlinear hysteretic parameters, as well as the unknown inputs. Further, a new adaptive technique based on improved particle swarm optimization is proposed to on-line track time-varying parameters, leading to structural damage identification, including the time, location and severity. Vibration experiment study was conducted using a base-isolated building model, nonlinear dynamic characteristics of which were described by the Bouc-Wen model. El Centro and Kobe earthquake excitations were used to drive the model by shaking table. Different damage scenarios were simulated and tested during the experiment, and then the AEFK-UI approach was used to track damages and identify unknown inputs based on the measured acceleration responses. The identification results correlate well with the referenced values. It is concluded that the proposed approach is capable of damage tracking and unknown input identification for the base-isolated structure.