Neural adaptive event-triggered prescribed-time formation control of multi-QUAVs systems with disturbances and actuator saturation

This paper investigates cooperative formation tracking control strategy of a multiple quadrotor unmanned aerial vehicles (multi-QUAVs) systems subject to mismatched disturbances and actuator saturation, aiming to ensure that each follower tracks the reference trajectory of the leader with a desired geometric configuration within a user-prescribed time. To address singularity problem that arises from the differentiation of scaling functions, a non-scaling virtual control law construction strategy is proposed, in which the time-varying scaling function is directly injected into the control channel as an external gain. Furthermore, RBFNNs are utilized as online approximators, and an anti-windup compensator is designed to reduce adverse effects of truncation errors induced by actuator saturation. Additionally, an event-triggered control is incorporated to effectively save communicational and computational resources, and exclusion of the Zeno phenomenon is rigorously proved. Stability analysis shows that all signals of the closed-loop system are bounded, and tracking error of every quadrotor unmanned aerial vehicle (QUAV) converges to an arbitrarily small neighborhood of origin within prescribed time, with the convergence being independent of initial conditions. Finally, two simulation examples of multi-QUAVs formation flight are presented to validate effectiveness and robustness of this work.