在下一代航空IMA系统中进行适应性 ε-贪探索以实现稳定的重新配置
Guodong Li1,2, Zheyan Liu1, Wentao Zhang1
1School of Software, Northwestern Polytechnical University, Xi'an, 710072, China.
Scientific reports
|October 30, 2025
概括
采用双对决深度Q网络与自适应性爱普西隆-贪探索 (D3QNAE) 的新方法提高了基于集装箱的航空集成模块化航空 (IMA) 系统的故障耐受性. 这种方法提高了稳定性,并减少了复杂环境中的重新配置时间.
科学领域:
- 航空航天工程 航空航天工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 下一代航空集成模块化航空 (IMA) 系统利用集装箱技术提高了资源利用率和灵活性.
- 基于容器的IMA架构增加了系统重新配置的复杂性,给故障容忍带来了挑战.
- 目前的手动和启发式重新配置方法不足以满足现代的故障容忍要求.
研究的目的:
- 为基于容器的IMA系统开发一种高效的嵌入式容器重新配置方法.
- 提高IMA系统在连续故障的情况下的适应性和故障耐受性.
- 解决复杂的大规模部署中现有的重新配置策略的局限性.
主要方法:
- 实现双重决斗深度Q网络与自适应性子-贪探索 (D3QNAE).
- 利用适应式勘探策略,在复杂的环境中产生稳定的重新配置政策.
- 在大规模部署场景 (500任务配置) 中评估方法.
主要成果:
- 与D3QN基线相比,D3QNAE将第一个可行的解决方案的时间减少了34%.
- 实现了15.6%更高的最大奖励值,表明更有效的策略.
- 在连续故障下证明了100%的迁移影响率,确保了系统稳定性.
结论:
- 拟议的D3QNAE方法显著提高基于容器的IMA系统的故障耐受性.
- 这种方法提供了更好的稳定性和更低的维护成本.
- D3QNAE为航空系统中复杂的重新配置挑战提供了强大的解决方案.
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