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Adaptive Observer-Based Neural Network Control for Multi-UAV Systems with Predefined-Time Stability
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DOI：10.3390/drones9030222.png)
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Abstract
En 中文
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.
Keywords:
multi-UAV systems
predefined-time formation
state observer
sliding-mode control
neural network