This invention discloses a
robust control method and
system for brain-controlled unmanned aerial vehicles (UAVs) based on a
large model, belonging to the field of brain-computer interface and UAV control technology. Specifically, it includes: acquiring EEG signals and identifying discrete brain-controlled event commands; inputting these commands into a
large model intent reconstruction module after sliding window
serialization encoding; outputting action candidates with multiple risk levels; performing
parsing verification,
risk assessment, and motion feasibility checks on the action candidates; selecting the final action and mapping it to parameterized motion skill atoms; having a state
machine manage the execution of the skill atoms; implementing closed-loop execution via the flight control interface and monitoring safety boundaries; and triggering hovering and return-to-home actions in case of anomalies. This invention achieves intent reconstruction and risk-layered decision-making for brain-controlled commands, constructing a full-link safety closed-
loop control system. It significantly improves the control stability, safety, and
system resilience of brain-controlled UAVs under uncertain command conditions, reduces the risk of false triggering, and increases the mission success rate.