Learning-Based Security Control of Unmanned Aerial Vehicle Swarm System With Multisource Disturbances and Cyber...

This article investigates the security control problem with guaranteed communication connection and collision avoidance (GCCA) for an unmanned aerial vehicle (UAV) swarm subject to multisource disturbances and cyber attacks. The network time-delay model of the communication link attacked is introduced first. To ensure communication, connection, and collision avoidance (CA), an improved artificial potential field (APF) function is developed. Then, a reinforcement learning (RL) model-free attitude controller scheme is proposed based on a disturbance suppression trajectory controller to accommodate the adverse effect of multisource disturbances, including matched and unmatched ones; meanwhile, it is combined with GCCA, which successfully accomplishes the control objectives. In particular, by utilizing the improved reciprocally convex combination approach, a sufficient criterion is given to guarantee that the UAVs satisfy the finite horizon H-infinity consensus performance. Finally, a comparative experiment is presented to verify the feasibility of the research.