用自适应模型预测控制提升无人机飞行效率，缩短飞行时间。
Flight Time Improvement Using Adaptive Model Predictive Control for Unmanned Aerial Vehicles
- 提出自适应模型预测控制，优化无人机飞行路径以减少耗时。
- 相比传统MPC，新方法在飞行时间上表现更优，提升系统效率。
- 适合关注无人机智能控制与高效路径规划的研究者。
智能空中平台如无人飞行器（UAV）有望在交通、交通管理、野外监测、工业生产及农业管理等领域带来变革。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，现有研究主要聚焦于轨迹跟踪和最小化飞行误差，对飞行时间优化关注较少。本文提出一种模型预测控制（MPC）方法，旨在最小化飞行时间，同时克服经典MPC控制器的局限性。文中详细介绍了该MPC方法及其在UAV控制中的应用。实验结果表明，所提控制器在效率方面优于标准MPC，且具备集成智能算法至基础控制器的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。