mlsanityData to 14 Oct 2026

Lyapunov Function-guided Reinforcement Learning for Flight Control

arXiv 2510.22840 ↗First posted 26 Oct 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

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.

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.

Builds on

3 of 17 references.

References · 3

  1. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  2. 2014Theoretical and Numerical Analysis of Approximate Dynamic Programming with Approximation Errorscited by 2
  3. 2017Stability Analysis of Optimal Adaptive Control using Value Iteration with Approximation Errorscited by 2

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