Fusing Deep Learning and Predictive Control for Safe Operation of Manned–Unmanned Aircraft Systems

With the rapid development of the low-altitude economy, the deployment of unmanned aircraft vehicles (UAVs) in many fields is increasing continuously, and the demand for collaborative flights is also growing. However, the issue of flight safety in complex airspace remains a pressing concern. Precise flight path prediction, collision detection, and avoidance are paramount for secure collaborative operations. This study proposes an integrated framework that combines an EKF-LSTM model for trajectory prediction, a Trajectory Dispersion Cone (TDC) method for probabilistic collision risk assessment, and a Velocity Obstacle-Model Predictive Control (VO-MPC) strategy for dynamic collision avoidance. Experimental results demonstrate the advantages of our approach: the EKF-LSTM model reduces prediction errors in complex flight states. Furthermore, the VO-MPC method achieves a 99.8% collision avoidance success rate under low-noise conditions—an 8.6% improvement over traditional MPC—while reducing the average collision probability by 66.7%. It also maintains stable performance under medium- and high-noise conditions, reducing the collision probability to only 27.7% and 34.2% of that of conventional MPC, respectively. The proposed framework offers a solution for safe manned–unmanned collaboration in complex environments. Future work will extend these methods to multi-aircraft cooperative scenarios.