Hawk-Pigeon Potential Game Collaborative Control for MAV-UAV Swarm Formation

This article proposes a hawk-pigeon game collaborative control scheme for manned-unmanned aerial vehicle (MAV-UAV) systems, effectively resolving human-machine decision conflicts through bioinspired equilibrium optimization derived from predator-prey behaviors. By mathematically translating the hunting tactics of a strategic hawk and the evasive, flocking behaviors of pigeons, we model the MAV as an apex guide selectively intervening in the UAV swarm, while the UAVs function as a cohesive flock executing formation tracking alongside predator-avoidance-like responses to MAV commands. A hawk-pigeon potential game framework is developed to quantify flight costs under position-velocity constraints, where the MAV's predatory-like influence over the flock is mathematically realized via reference state modulation. An incremental nonlinear dynamic inversion controller decouples cost evaluation from control computation. To alleviate MAV workload, a Bargaining game among UAVs allocates intervention authority based on deviation severity. Finally, the equilibrium solutions of both games are rigorously analyzed and proved. Numerical simulations demonstrate significant improvements in tracking precision, robustness, and convergence speed over conventional model predictive control, thereby establishing a biologically grounded methodology for human-swarm collaboration.