Data-Driven Robust Optimal Guidance With Input Saturation via Differential Graphical Game Strategy for Cooperative Aerial Vehicles

In this paper, a robust optimal three-dimensional cooperative guidance law with input saturation is proposed for intelligent aerial vehicles to intercept an unknown maneuvering target. The problem of cooperative interception is formulated as a leader-follower optimal tracking control problem based on the differential graphical game subject to a nonautonomous leading vehicle with bounded control inputs. Utilizing the backstepping method, the guidance law is divided into a feedforward part for generating the desired state signals and compensates for the impact of input saturation, and a data-driven feedback part based on the differential graphical game that regulates tracking errors due to unknown target maneuvers while optimizing interactive performance indices. The uniform ultimate bounded property of the tracking errors in the closed-loop system can be guaranteed and the predefined interactive cost function can be optimized by the proposed guidance law. Simulation examples of cooperative interception are provided to validate the effectiveness of the proposed approach.