Collision-Free Optimal Tracking Control for AAV-AGV Formation Against Byzantine Attacks

This paper explores the issue of collision-free optimal tracking control of unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) formation under Byzantine attacks. Based on collision-free margin and collision risk angle, a collision avoidance scheme for UAV-UGV swarms is proposed, which takes into account both external and internal aspects of the formation. Meanwhile, considering that the UAV-UGV swarm is susceptible to the propagation of incorrect neighbors’ information and false input signals (called Byzantine attacks), which is effectively reinterpreted as the management of unknown variables within the control inputs. Then, a barrier function and a control force direction function are introduced in the optimal performance index for collision avoiding with each system’s radius, and a collision-free optimal control strategy under the reinforcement learning (RL) algorithm is investigated. Furthermore, the neural network is utilized to model the Byzantine attacks and appropriate unknown factors arising from Hamilton-Jacobi-Bellman (HJB) equation, the actor and critic adaptive laws are presented in actor-critic-identifier architecture. Subsequently, a collision-free optimal tracking control scheme is proposed to ensure safe collaborative moving of the UAV-UGV formation under Byzantine attacks. Finally, simulations are performed to validate the effectiveness of the proposed approach.