An Algorithm Design for Constrained Noncooperative Games and Its Application to UAV Swarm

This paper introduces a novel adaptive algorithm for seeking the Nash equilibrium (NE) in noncooperative games with local inequality constraints. Our work is distinguished by the introduction of an adaptive penalty technique, which ensures that players' decisions adhere to the inequality constraint set without necessitating prior global information for penalty parameters. This algorithm achieves full distribution and independence from predefined parameters, ensuring ease of implementation in practical scenarios. Lastly, the theoretical results are validated through a simulation involving an unmanned aerial vehicle (UAV) swarm game.