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基于ESO的自适应神经网络控制用于防风和有效载荷干扰的四旋翼机
1School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China. caixin94@outlook.com.
Scientific reports
|February 7, 2026
概括
本研究提出了一个强大的反干扰控制框架,用于面临多重干扰的四旋翼无人机 (UAV). 拟议的方法通过使用扩展状态观测器 (ESO) 和神经网络进行干扰估计和控制来增强轨迹跟踪.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 低调的四旋翼无人机 (UAV) 容易因风和有效载荷变化等外部干扰而显著降低性能.
- 现有的控制策略往往需要精确的动态模型,或者难以同时解决多个未知的干扰.
- 强大的轨迹跟踪对于复杂环境中安全有效的无人机操作至关重要.
研究的目的:
- 设计一个强大的反干扰控制框架,用于低调的四旋翼无人机.
- 开发一种适应性跟踪控制器,可以补偿一次性干扰,而不需要精确的动态模型.
- 通过模拟和物理实验验证拟议的控制策略.
主要方法:
- 建立了四旋翼无人机的动态模型,结合了风和有效载荷的干扰.
- 用单个参数的扩展状态观察器 (ESO) 来积极估计一次性干扰.
- 设计了一个基于自适应神经网络的控制器,重量通过自适应定律进行调整.
- 使用利亚普诺夫稳定理论分析了ESO的有限时间收和闭环系统的统一终极边界性.
主要成果:
- 与LADRC,SMC,MFTSMC和ADFOC相比,在各种干扰场景下,拟议的控制战略在轨迹跟踪方面表现优越.
- 模拟证实了ESO在准确估计一次性干扰方面的有效性.
- 适应神经网络控制器成功地弥补了模型不确定性和外部干扰.
- 物理实验验证实了开发的控制方法的实用性和稳定性.
结论:
- 拟议的反干扰控制框架,集成ESO和神经网络,提供了一个强大的解决方案,用于轨迹跟踪在Underactuated四旋翼无人机.
- 该方法有效地处理多个干扰,并减少对精确动态模型知识的依赖.
- 经过验证的方法具有显著的潜力,可以在现实应用中提高无人机系统的可靠性和性能.
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