Ensuring Safety for UAVs Through Event-Triggered Predictive Control

This paper presents a control system for un-manned aerial vehicles (UAVs) that track deformable objects. Using Event-Triggered (ET) principles and Image Moments-Based Nonlinear Model Predictive Control (NMPC), the suggested approach improves flexibility by triggering control changes in response to specific occurrences. The incorporation of Barrier Functions (BFs) enforces safety requirements, which are critical for negotiating uncertain terrains. The framework's implementation and testing on a UAV tracking deformable coastlines are intended to demonstrate its effectiveness in adapting to dynamic situations while adhering to safety limitations.