Intelligent aerial platforms such as Unmanned Aerial Vehicles (UAVs) are expected to revolutionize various fields, including transportation, traffic management, field monitoring, industrial production, and agricultural management. Among these, precise control is a critical task that determines the performance and capabilities of UAV systems. However, current research primarily focuses on trajectory tracking and minimizing flight errors, with limited attention to improving flight time. In this paper, we propose a Model Predictive Control (MPC) approach aimed at minimizing flight time while addressing the limitations of the commonly used classical MPC controllers. Furthermore, the MPC method and its application for UAV control are presented in detail. Finally, the results demonstrate that the proposed controller outperforms the standard MPC in terms of efficiency. Moreover, this approach shows potential to become a foundation for integrating intelligent algorithms into basic controllers.

翻译：无人机等智能空中平台有望在交通运输、交通管理、野外监测、工业生产和农业管理等多个领域引发革命性变革。其中，精确控制是决定无人机系统性能与能力的关键任务。然而，当前研究主要集中于轨迹跟踪与飞行误差最小化，对提升飞行时间的关注较为有限。本文提出一种旨在最小化飞行时间的模型预测控制方法，同时解决了常用经典MPC控制器的局限性。此外，文中详细阐述了MPC方法及其在无人机控制中的应用。最终实验结果表明，所提出的控制器在效率方面优于标准MPC方法。该方案还有望成为将智能算法集成至基础控制器的潜在技术基础。