Parallel crosschecking neural network based fault-tolerant flight parameter estimation and faulty sensor identification

Reliable flight parameter estimation is crucial for the safe operation of unmanned air vehicles. Recent studies on flight parameter estimation based on distributed airflow sensors have shown promising results; however, how to extract reliable flight parameters and locate faults in case of sensor failures are not studied yet. This paper proposes a novel fault-tolerant flight parameter estimation method with capabilities to identify and isolate faulty sensors for a flight parameter estimation system based on distributed airflow sensors. First, the distributed sensors are grouped into subsets using combinations. Second, feedforward backpropagation neural networks of the same structure are designed and used by all subsets for flight parameter estimation. The crosschecking between these parallel networks improves the robustness of the estimation. In case of sensor failures, the sensor groups that contain any faulty sensors will result in wrong flight parameter estimations. Especially, a directional random sample consensus algorithm is developed and used to identify the subsets that contain faulty sensors. Then, the ratio of the faulty subsets is used to identify the number of faulty sensors. Finally, the faulty sensors will be identified according to the frequencies of their appearance in the subsets that contain faulty sensors. The effectiveness of the proposed method is validated by using wind tunnel tests. The results show that the proposed method can effectively provide reliable flight parameters and identify faulty sensors.