用深度强化学习提升航空航天安全关键系统的控制能力
Deep Reinforcement Learning Based Systems for Safety Critical Applications in Aerospace
- 采用深度强化学习构建飞行与发动机控制新架构
- 支持实时感知与故障自适应，提升系统可靠性
- 适用于自主飞行与人机协同控制场景
近年来，人工智能在航空航天领域的应用取得显著进展，尤其体现在控制系统方面。随着高性能计算（HPC）平台的持续发展，其有望取代现有飞行或发动机控制计算机，提供更强的计算能力。这一演进将使图像处理、缺陷检测等实时AI应用无缝集成至监控系统，实现实时态势感知和增强的故障检测与容错能力。此外，人工智能在航空航天控制中的潜力不仅限于完全自主，还可通过辅助功能增强人类操作。深度强化学习（DRL）在控制系统的应用，无论是在全自主运行还是作为增强工具方面，均能带来显著性能提升。
原文摘要 · Abstract (English)
Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities. This shift will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation. Furthermore, AI's potential in aerospace extends to control systems, where its application can range from full autonomy to enhancing human control through assistive features. AI, particularly deep reinforcement learning (DRL), can offer significant improvements in control systems, whether for autonomous operation or as an augmentative tool.
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