Fault detection in flight control systems based on the generalized variance of the Kalman filter innovation sequence

1999 ◽  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Control Systems ◽  
Flight Control ◽  
Generalized Variance

Fault diagnosis for flight control systems using subspace method and adaptive two-stage Kalman filter

2016 ◽  
Vol 38 (12) ◽  
pp. 1480-1490 ◽  
Author(s):  
Jianchen Wang ◽  
Xiaohui Qi
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Control Systems ◽  
Linear Model ◽  
Flight Control ◽  
Model Identification ◽  
Identification Algorithm ◽  
Actuator Fault ◽  
Two Stage ◽  
Non Linear

Model-based fault diagnosis has attracted considerable attention from researchers and developers of flight control systems, thanks to its hardware simplicity and cost-effectiveness. However, the airplane model, which is adopted commonly in fault diagnosis, only exists theoretically and is linearized in approximation. For this reason, uncertainties such as system non-linearity and subjectivity will degrade the fault diagnosis results. In this paper, we propose a novel actuator fault diagnosis scheme for flight control systems based on model identification techniques. With this scheme, system identification can be achieved with a linear model that uses a closed-loop subspace model identification algorithm, and a non-linear model that uses an extended state observer and neural networks. On this basis, the current actuator fault is estimated using an adaptive two-stage Kalman filter. Finally, the non-linear six-degree-of-freedom model of a B747 airplane is simulated in the Matlab/Simulink environment, where the effectiveness of the proposed scheme is verified from fault diagnosis tests.

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