Adaptive Observer-Based Neural Network Control for Multi-UAV Systems with Predefined-Time Stability

This article proposes an observer-based predefined-time robust formation controller for uncertain multi-UAV systems with external disturbances by integrating the sliding-mode technique with neural networks. The predefined-time strategy is developed to enhance formation tracking performance, including faster convergence speed, higher accuracy, and better robustness, while the sliding-mode scheme, integrated with the neural network, is effectively utilized to handle uncertain dynamics and external disturbances, ensuring adaptivity, availability, and robustness. Furthermore, the stability of the closed-loop control system is analyzed using Lyapunov’s method applied to the formulation of the quadrotor Newton–Euler model. This analysis fully guarantees that the desired formation position tracking and attitude stabilization goals for multi-UAV (quadrotor) systems can be achieved. Finally, the effectiveness of the theoretical results is validated through comprehensive simulations.