A Simulation-Driven Hierarchical Stackelberg-DMPC Framework for UAV Swarm Interception

This paper proposes a simulation-driven hierarchical Stackelberg–distributed model predictive control framework (SHS-DMPC) for intercepting multi-wave UAV swarm attacks under limited defensive resources. The interaction between the defender and the attacker is modeled as a Stackelberg leader–follower game. At the strategy layer, a finite-response approximation of Stackelberg decision making is constructed under incomplete information: the attacker’s response type is inferred online from swarm-level motion features, and candidate defender strategies are subsequently evaluated through state-dependent short-horizon rollout simulations. This formulation avoids requiring explicit knowledge of the attacker’s utility function while retaining anticipatory leader–follower strategy evaluation. At the task-allocation layer, target value, threat level, spatial bias, and a reassignment penalty are incorporated into the allocation cost to translate the selected defense strategy into dynamic defender–attacker assignments. At the control layer, each defending UAV solves a local DMPC problem to generate continuous control inputs while satisfying kinematic, inter-UAV separation, and airspace-boundary constraints. Simulation results show that SHS-DMPC achieve a higher interception success rate, a lower value-weighted target loss rate, and fewer minimum-separation violations than the comparison methods under multi-wave heterogeneous attack scenarios, demonstrating the benefits of closed-loop coupling among response inference, strategy-conditioned allocation, and constraint-aware distributed trajectory optimization.