VTOL无人机的合作追踪控制:一个固定时间的超级扭转方法
IEEE transactions on cybernetics
|February 10, 2026
概括
本研究介绍了多个VTOL无人机的自适应神经网络控制策略,确保精确的合作跟踪,尽管复杂的动态和通信限制. 这种新的方法保证了快速融合,并减少了网络使用,以实现高效的无人机操作.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 网络化自主系统 网络化自主系统
- 航空航天工程 航空航天工程
背景情况:
- 由于非线性动态,干扰和有限的带宽,多个VTOL无人机的合作跟踪是复杂的.
- 现有的控制方法往往无法解决所有挑战,也不能保证固定时间的融合.
- 在滑动分流器上控制聊天进一步使无人机控制复杂化.
研究的目的:
- 为合作无人机跟踪开发一种新的自适应神经网络事件触发的固定时间超扭转 (ANEFS) 控制策略.
- 为了提高跟踪精度和融合速度,同时最大限度地减少网络通信.
- 确保对系统非线性和不确定性的稳定性.
主要方法:
- 使用双重闭环层次控制框架.
- 一个基于辅助变量的分布式固定时间估计器 (ADFE) 设计用于准确的领导者轨迹估计.
- 一个事件触发的固定时间超扭转 (EFST) 控制规律和一个自适应神经网络固定时间超扭转 (ANFST) 扭矩控制器被实现.
主要成果:
- 拟议的ANEFS战略实现了对VTOL无人机的快速和准确的合作位置和态度跟踪.
- 事件触发机制显著减少了网络通信带宽的使用.
- 控制策略证明了对系统非线性和外部干扰的稳定性.
结论:
- 开发的ANEFS控制方法为网络环境中的合作无人机跟踪提供了快速,稳健和资源高效的解决方案.
- 该研究通过数值模拟和使用Crazyflie四旋翼飞机的现实世界飞行实验来验证有效性.
- 这项工作在复杂的操作场景中提高了多无人机系统的能力.
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