带有不准确测量噪声的最小二乘故障估计（英文）

On least squares fault estimation with incorrect measurement noise statistics

针对一类带有事件触发测量传输和不准确测量噪声统计特性的系统,研究了最小二乘故障估计及其性能分析。通常情况下,测量噪声的统计特性不是完全已知的。提出一种事件触发机制,当预先设定的条件被破坏即事件被触发时,测量输出会传到远端估计器。在满足使得滤波误差协方差最小的意义下,设计了滤波器,其参数通过最小二乘的方法在线迭代得到,然后进一步分析了带有不准确测量噪声的滤波器性能。最后,利用具有实际背景的数值仿真例子说明了所提算法的有效性以及带有不准确测量噪声对于估计性能的差异影响。

The least squares fault estimation and performance analysis problem was investigated for a class of systems with event-triggered measurement transmission and incorrect measurement noise statistics. Usually, measurement noise statistics was not completely known. In this case, an event-triggered scheme was proposed to transmit the measurement output to remote estimator when a pre-set condition was violated and an event was triggered. A filter was designed in order to minimize an upper bound of filtering error covariance with event-triggered measurement transmissions and incorrect measurement noise statistics. The desired filter parameters were calculated recursively for online computation in the framework using the least squares method. Performance of the proposed filter with incorrect measurement noise statistics was further studied and analyzed. A numerical simulation with actual background was exploited to illustrate the effec- tiveness of the proposed algorithm and demonstrate the performance difference.