Reinforcement Learning-Based Optimal Formation Tracking for UAVs With Safety Constraints

Summary

This article presents a reinforcement learning scheme for safe optimal formation tracking of multiple fixed-wing UAVs under external disturbances and asymmetric control constraints. The method builds a safe set from a control barrier function, integrates it into a nonquadratic cost of a constrained zero-sum differential game, and learns the robust safe Nash policy with a critic-only RL strategy using experience replay, avoiding the persistence of excitation condition. The authors verify stability and forward invariance of the safe set and validate the scheme in simulation.