Fault Tolerant Flight Control: An Application of the Fully Connected Cascade Neural Network

Abstract

The endurance of an aircraft can be increased in the presence of failures by utilising flight control systems that are tolerant to failures. Such systems are known as fault tolerant flight control systems (FTFCS).
FTFCS can be implemented by developing failure detection, identification and accommodation (FDIA) schemes. Two of the major types of failures in an aircraft system are the sensor and actuator failures. In this research, a sensor failure detection, identification and accommodation (SFDIA); and an actuator failure detection, identification and accommodation (AFDIA) schemes are developed. These schemes are developed using the artificial neural network (ANN).
A number of techniques can be found in the literature that address FDIA in aircraft systems. These techniques are, for example, Kalman filters, fuzzy logic and ANN.
This research uses the fully connected cascade (FCC) neural network (NN) for the development of the SFDIA and AFDIA schemes. Based on the study presented in the literature, this NN architecture is compact and efficient in comparison to the multi-layer perceptron (MLP) NN, which is a popular choice for NN applications. This is the first reported instance of the use of the FCC NN for fault tolerance applications, especially in the aerospace domain.
For this research, the X-Plane 9 flight simulator is used for data collection and as a test bed. This simulator is well known for its realistic simulations and is certified by the Federal Aviation Administration (FAA) for pilot training. The developed SFDIA scheme adds endurance to an aircraft in the presence of failures in the aircraft pitch, roll and yaw rate gyro sensors. The SFDIA scheme is able to replace a faulty gyro sensor with a FCC NN based estimate, with as few as 2 neurons. In total, 105 failure experiments were conducted, out of which only 1 went undetected.
In the developed AFDIA scheme, a FCC NN based roll controller is employed, which uses just 5 neurons. This controller can adapt on-line to the post failure dynamics of the aircraft following a 66\% loss of wing surface. With 66\% of the wing surface missing, the NN based roll controller is able to maintain flight. This is a remarkable display of endurance by the AFDIA scheme, following such a severe failure. The results presented in this research validate the use of FCC NNs for SFDIA and AFDIA applications.