Fault diagnosis of a Fixed Wing UAV Using Hardware and Analytical Redundancy
In unmanned aerial systems an autopilot controls the vehicle without human interference. Modern autopilots use an inertial navigation system, GPS, magnetometers and barometers to estimate the orientation, position, and velocity of the aircraft. In order to make correct decisions the autopilot must rely on correct information from the sensors.
Fault diagnosis can be used to detect possible faults in the technical system when they occur. One way to perform fault diagnosis is model based diagnosis, where observations of the system are compared with a mathematical model of the system. Model based diagnosis is a common technique in many technical applications since it does not require any additional hardware. Another way to perform fault diagnosis is hardware diagnosis, which can be performed if there exists hardware redundancy, i.e. a set of identical sensors measuring the same quantity in the system.
The main contribution of this master thesis is a model based diagnosis system for a fixed wing UAV autopilot. The diagnosis system can detect faults in all sensors on the autopilot and isolate faults in vital sensors as the GPS, magnetometer, and barometers. This thesis also provides a hardware diagnosis system based on the redundancy obtained with three autopilots on a single airframe. The use of several autopilots introduces hardware redundancy in the system, since every autopilot has its own set of sensors. The hardware diagnosis system handles faults in the sensors and actuators on the autopilots with full isolability, but demands additional hardware in the UAV.
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