非线性系统传感器偏差的自适应故障检测与分离方法

针对一类具有模型不确定性且状态部分可测的非线性系统，提出了一种基于鲁棒自适应技术的传感器偏差故障检测与分离方法。所提出的故障检测与分离方法是由一个故障检测估计器和一组故障分离估计器完成的，其中每一个故障分离估计器对应一种类型的传感器偏差故障。对于某一故障分离估计器如果它的的输出估计误差中至少一项的绝对值超过相应的自适应阈值，则这个故障发生的可能性被排除，即可分离故障。在系统存在模型不确定性和状态部分可测的情况下，应用自适应技术来估计未知的传感器偏差故障并分析了故障分离器的稳定性。仿真算例证明了所提出的鲁棒自适应故障检测与分离方法的有效性。

Adaptive Fault Detection and Isolation Approach for Sensor Bias in Nonlinear Systems

The robust adaptive sensor bias fault detection and isolation （FDI） scheme were proposed against a class of nonlinear systems with unstructured modeling uncertainty and partial states available for measurement. The proposed fault FDI scheme consisted of a fault detection estimator and a bank of fault isolation estimators, each corresponding to a type of sensor bias fault. A fault isolation decision scheme was presented with guaranteed performance. If at least one component of the modulus of the output estimation error of a fault isolation estimator exceeded the corresponding adaptive threshold at some finite time, the occurrence of that type of fault can be excluded, so fault isolation was achieved. Adaptive techniques to estimate an unknown sensor bias fault were used in the presence of modeling uncertainties and partial state measurement. The stability properties of the sensor bias fault isolation eatimator were rigorously analyzed. A simulation example was suggested to illustrate the effectiveness of the robust adaptive fault detection and isolation scheme proposed.