基于神经网络的可视化振动控制,用于像子一样的灵活的飞翼.
Hejia Gao1, Jinxiang Zhu2, Changyin Sun3
1School of Artificial Intelligence, Anhui University, Hefei 230601, China; Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui 230601, China.
ISA transactions
|June 4, 2024
概括
这项研究引入了一种新的控制方法,以减少灵活的飞翼的振动,提高它们的稳定性和性能,用于各种应用. 适应式控制器可确保无人机系统的可靠飞行.
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
- 生物模拟学是一种生物模拟学.
背景情况:
- 灵活的飞翼提供优势,如低能耗,但遭受振动和变形.
- 有效的振动控制对于保持这些系统的性能和稳定性至关重要.
研究的目的:
- 开发一个动态可视化模型,用于灵活的飞翼.
- 设计一个自适应式振动控制器,以解决系统不确定性和执行器故障.
主要方法:
- 改进的刚性有限元素方法 (IRFE) 用于动态建模.
- 开发了一种结合非单元终端滑动模式 (NTSM) 控制和模糊神经网络 (FNN) 的自适应控制器.
- 为了确保闭环系统的稳定性,应用了利亚普诺夫稳定性理论.
主要成果:
- 拟议的控制器有效地抑制了振动,并实现了稳定的轨迹跟踪.
- 模拟显示了控制器对系统不确定性和执行器故障的稳定性.
- 该方法在军事和民用应用中显示出了显著的实际价值.
结论:
- 开发的自适应式振动控制器提高了灵活的飞翼系统的可靠性和性能.
- IRFE建模和NTSM-FNN控制策略为振动管理提供了一个强大的解决方案.
- 这项技术在无人机系统中广泛适用于各种任务.
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