# Lyapunov Function-guided Reinforcement Learning for Flight Control

arXiv: https://arxiv.org/abs/2510.22840 · HTML: https://mlsanity.com/paper/2510.22840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 3 of 3)

- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [Theoretical and Numerical Analysis of Approximate Dynamic Programming with Approximation Errors](https://mlsanity.com/paper/1412.6095/) (2014)
- [Stability Analysis of Optimal Adaptive Control using Value Iteration with Approximation Errors](https://mlsanity.com/paper/1710.08530/) (2017)

## Abstract (arXiv)

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.