Lately, Morphing Aerial Systems (MASs) have seen a surge in demand due to their exceptional maneuverability, flexibility, and agility in navigating complex environments. Unlike conventional drones, MASs boast the ability to adapt and alter their morphology during flight. However, managing the control and stability of these innovative and unconventional vehicles poses a significant challenge, particularly during the aerial transformation phases. To solve this problem, this manuscript proposes a Radial Basis Function Artificial Neural Network-Based Fast Terminal Sliding Mode Control (RBFANN-FTSMC) method. This approach is designed to effectively manage morphology changes, ensure precise trajectory tracking, and mitigate the impact of external disturbances and parameter uncertainties. Accordingly, the RBFANN-FTSMC will be evaluated against Proportional Integral Derivative (PID), Sliding Mode (SM), and Fast Terminal Sliding Mode (FTSM) controllers through two flight simulation scenarios to validate its effectiveness. Additionally, the control parameters will be optimized using a recent metaheuristic algorithm known as the Whale Optimization Algorithm (WOA). A novel hardware control diagram is explained. Finally, the ability to alter morphologies and the results of experimental tests are discussed to highlight the performance and limitations of the mechanical structure and the implemented RBFANN-FTSMC.

中文翻译：

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基于径向基函数人工神经网络的快速终端滑模变形无人机轨迹跟踪控制：理论与实验


最近，Morphing Aerial Systems （MAS） 因其在导航复杂环境方面的卓越机动性、灵活性和敏捷性而需求激增。与传统无人机不同，MAS 拥有在飞行过程中适应和改变其形态的能力。然而，管理这些创新和非常规车辆的控制和稳定性是一项重大挑战，尤其是在空中改造阶段。针对这一问题，本文提出了一种基于径向基函数人工神经网络的快速终端滑模控制 （RBFANN-FTSMC） 方法。这种方法旨在有效管理形态变化，确保精确的轨迹跟踪，并减轻外部干扰和参数不确定性的影响。因此，RBFANN-FTSMC 将通过两个飞行模拟场景与比例积分微分 （PID）、滑动模式 （SM） 和快速终端滑动模式 （FTSM） 控制器进行评估，以验证其有效性。此外，将使用一种称为 Whale 优化算法 （WOA） 的最新元启发式算法来优化控制参数。解释了一种新颖的硬件控制图。最后，讨论了改变形态的能力和实验测试的结果，以突出机械结构和实施的 RBFANN-FTSMC 的性能和局限性。