Fixed-Time Networked UAV Topology Reconfiguration With Disturbance Rejection via Deep Reinforcement Learning

The unmanned aerial vehicle (UAV) plays an indispensable role in catalyzing the advancement of the low-altitude economy (LAE). However, the existing UAV motion control strategies are vulnerable and susceptible to specified time constraints, unknown environmental interferences, dynamic topology and sensor malfunctions. To address the aforementioned issues, this paper proposes a deep reinforcement learning (DRL)-based fixed-time formation control strategy for networked UAVs with topology reconfiguration capability in the presence of wind disturbances. Firstly, a DRL-driven topology reconfiguration algorithm is designed, leveraging a deep deterministic policy gradient, to address communication channel loss in UAV formation networks caused by sensor faults, which can be achieved by determining the optimal topology from a set of directed graphs. Furthermore, based on a simple filtering operation on state variables and invariant manifolds, an unknown system dynamics estimator (USDE) is developed for UAVs to counteract external wind disturbances effectively. Finally, a fixed-time UAV formation controller with disturbance rejection is elaborated to propel UAVs to converge to the desired trajectory within an explicit time, independent of their initial conditions. Simulation results validate the effectiveness of the proposed method.