Abstract:
Reliable operation of unmanned aerial vehicles (UAVs) in uncertain and adverse conditions
remains a critical challenge, particularly in the presence of icing, disturbances, and dynamic in-
teractions in multi-agent systems. This thesis develops an integrated framework that combines
probabilistic estimation and nonlinear control strategies to enhance performance and robust-
ness. An approach based on the particle filter (PF) is employed to improve state estimation ac-
curacy and detect icing-related faults by analyzing variations in system parameters. For robust
trajectory tracking in uncertain flight conditions, the proposed control scheme combines a high-order sliding mode observer (HOSMO) with the super-twisting algorithms (STA), effectively
managing disturbances and model variations. At the multi-agent level, a distributed control
strategy is introduced, utilizing finite-time observers and controllers within a leader–follower
structure to enable fast and coordinated group behavior. The thesis demonstrates the effec-
tiveness of the proposed methods in enhancing fault detection capabilities, control robustness,
and ensuring reliable multi-UAV coordination.
