实时联合估计系统状态、参数与未知输入，提升故障诊断精度
An unscented Kalman filter method for real time input-parameter-state estimation
- 分两阶段迭代估计未知输入，结合预测与测量修正
- 仅需输出数据即可唯一识别系统，适用于零或非零已知输入
- 适合需要实时状态感知的工程系统，如机械振动监测
本文研究了一种新型无迹卡尔曼滤波方法在线性与非线性系统中的输入-参数-状态联合估计能力。未知输入在每个时间步内分两阶段估计：第一阶段利用预测的状态和系统参数生成输入初值；第二阶段结合实测数据修正后的状态与参数，获得最终输入估计。通过摄动分析证明，只要系统存在一个已知为零或非零的输入，即可实现系统的唯一辨识。该纯输出方法相比传统仅输出参数辨识策略，能同时实时估计动态状态、系统参数与输入，显著提升对系统行为的理解。
原文摘要 · Abstract (English)
The input-parameter-state estimation capabilities of a novel unscented Kalman filter is examined herein on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system parameters provide an estimation of the input. Secondly, the corrected with measurements states and parameters provide a final estimation. Importantly, it is demonstrated using the perturbation analysis that, a system with at least a zero or a non-zero known input can potentially be uniquely identified. This output-only methodology allows for a better understanding of the system compared to classical output-only parameter identification strategies, given that all the dynamic states, the parameters, and the input are estimated jointly and in real-time.
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