用李雅普诺夫函数引导强化学习，提升飞行控制稳定性与平滑性。
Lyapunov Function-guided Reinforcement Learning for Flight Control
- 基于李雅普诺夫函数设计在线学习飞行控制器，优化动作平滑性。
- 通过增量模型误差分析，验证系统在离散化下的收敛性能。
- 适合关注飞行控制稳定性与强化学习融合的工程研究者。
本文提出一种分层在线学习飞行控制系统，并针对动作平滑性进行改进。研究聚焦于控制系统的收敛性能，以李雅普诺夫函数候选量的增量作为衡量指标。该指标推导考虑了增量模型引入的离散化误差与状态预测误差。通过飞行控制仿真对比，验证了方法的有效性。
原文摘要 · Abstract (English)
A cascaded online learning flight control system has been developed and enhanced with respect to action smoothness. In this paper, we investigate the convergence performance of the control system, characterized by the increment of a Lyapunov function candidate. The derivation of this metric accounts for discretization errors and state prediction errors introduced by the incremental model. Comparative results are presented through flight control simulations.
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