Abstract:
Operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation
authorities which require full compliance with regulations that apply for manned aircraft.
This thesis proposes control algorithms for a collision avoidance system that can be used
as an advisory system or a guidance system for UAVs that are flying in civil airspace under
visual flight rules. An effective collision avoidance system for the UAV should be able to
perform the different functionalities of the pilot in manned aircraft. Thus, it should be able
to determine, generate, and perform safe avoidance manoeuvres. However, the capability to
generate resolution advisories is crucial for the advisory systems. A decision making system
for collision avoidance is developed based on the rules of the air. The proposed architecture
of the decision making system is engineered to be implementable in both manned aircraft
and UAVs to perform different tasks ranging from collision detection to a safe avoidance
manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are
proposed based on pilot suggestions for a subset of possible collision scenarios. The avoidance
manoeuvre generation algorithm is augmented with pilot experience by using fuzzy
logic technique to model pilot actions in generating the avoidance manoeuvres. Hence, the
generated avoidance manoeuvres mimic the avoidance manoeuvres of manned aircraft. The
proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal
collision avoidance algorithm is developed for real-time local trajectory planning. Essentially,
a finite-horizon optimal control problem is periodically solved in real-time hence
updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal
control problem is formulated in output space, and parameterised by using B-splines.
Then the optimal designed outputs are mapped into control inputs of the system by using
the inverse dynamics of a fixed wing aircraft.
