基于信息流的无人机飞行控制适应性近似建模,用于扰乱泛化
Runzhu Wang1, Yi Li1, Mengfan Liu1
1School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, 510275, China.
ISA transactions
|December 31, 2025
概括
本研究引入了基于信息流量元模型的自适应近似建模 (IFM-AAM) 框架,以增强无人机 (UAV) 飞行控制系统对抗各种威胁. 这种新的方法提高了控制性能和在各种干扰下实时适应能力.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统工程 控制系统工程
- 网络安全 网络安全
背景情况:
- 无人驾驶飞行器 (UAV) 飞行控制系统面临着环境干扰,组件故障和网络攻击等安全威胁.
- 由于特定的建模假设,当前的容错控制方法在异质扰动,特别是对抗性网络攻击的概括性方面扎.
研究的目的:
- 提出一个新的基于信息流量元模型的自适应近似建模 (IFM-AAM) 框架.
- 解决现有方法在无人机飞行控制中表示和减轻各种安全威胁方面的局限性.
- 提高控制系统对异质扰动的普遍性和适应性.
主要方法:
- 开发了一个信息流程元模型 (IFM) 用于高层结构抽象和近似建模.
- 在统一的信息流结构中以象征性异常表示异质扰动,以便进行一致的分析.
- 集成了一个轻量级的Actor-Critic强化学习 (RL) 机制,用于实时适应调整域模型 (DM) 中的控制参数.
主要成果:
- 通过比较模拟,IFM-AAM框架在名义和多种扰动场景中有效地近似了控制性能.
- 验证了对异质扰动的统一表示和传播分析.
- 机载计算机验证证实了开发的域模型的实时性能和低计算开销.
结论:
- 拟议的IFM-AAM框架提供了一种有效的统一方法,用于表示和分析无人机飞行控制中的异质扰动.
- 该框架提高了控制系统的适应性和强度,以应对广泛的威胁.
- 该方法为无人机中的实时应用提供了计算效率高的解决方案.
相关概念视频
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01:22Multi-input and Multi-variable systems
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01:26PD Controller: Design
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01:07Time-Domain Interpretation of PD Control
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01:26Feedback control systems
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01:22Controller Configurations
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