用强化学习设计飞机失稳恢复系统，提升训练机安全性和效率。
An Aircraft Upset Recovery System with Reinforcement Learning
- 采用SAC强化学习框架，结合专家设计的惩罚机制
- 在飞行员激活下实现更优的失稳恢复行为
- 适合飞行控制系统研发与智能航空安全领域研究者
本文探讨了为先进喷气式教练机开发飞行员激活恢复系统（PARS）的进展，该系统利用人工智能（AI）以提升操作效率。PARS模型采用先进的强化学习（RL）架构，融合前沿的软演员-评论家（SAC）算法和超参数优化方法。系统还纳入了控制工程师与领域专家提出的负加速度惩罚及其他手工设计特征。经专家评估，该AI模型的行为表现优于传统控制方法。
原文摘要 · Abstract (English)
This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers that utilizes artificial intelligence (AI) in an effort to enhance operational efficiency. The PARS model employs an advanced reinforcement learning (RL) architecture, incorporating a cutting-edge soft-actor critic (SAC) model and hyper-parameter optimization methods. Negative-g punishments and other handcrafted features remarked upon by control engineers and domain experts regarding PARS are also taken into account by the system. When evaluated by them, the AI model's behavior is deemed more desirable than that of conventional control methods.
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