Cooperative Tracking Control of VTOL Drones: A Fixed-Time Super-Twisting Approach

This study addresses the cooperative tracking control for multiple vertical take-off and landing (VTOL) drones operating in networked environments. The problem is particularly challenging due to the strongly nonlinear dynamics of drones, uncertain time-varying disturbances, limited communication bandwidth, and control chattering on the first-order sliding manifold. Existing approaches often address only part of these challenges or lack rigorous fixed-time convergence guarantees. To tackle these issues, this article proposes a novel adaptive neural network event-triggered fixed-time super-twisting (ANEFS) control strategy within a double closed-loop hierarchical framework. In the outer loop, a novel auxiliary variable-based distributed fixed-time estimator (ADFE) is designed, which, unlike conventional asymptotic estimators, guarantees fast and accurate estimation of the leader's trajectory. This is integrated into an event-triggered fixed-time super-twisting (EFST) control law that ensures precise position tracking while significantly reducing network usage. In the inner loop, an adaptive neural network fixed-time super-twisting (ANFST) torque controller robustly handles complex system nonlinearities and uncertainties, ensuring rapid attitude tracking. The effectiveness of the proposed method in providing fast, robust, and resource-efficient cooperative tracking is demonstrated through both numerical simulations and real-world flight experiments using lightweight Crazyflie quadcopters.