Improving Action Smoothness for a Cascaded Online Learning Flight Control System

2025-07-06 · Yifei Li, Erik-jan van Kampen arxiv

This paper aims to improve the action smoothness of a cascaded online learning flight control system. Although the cascaded structure is widely used in flight control design, its stability can be compromised by oscillatory control actions, which poses challenges for practical engineering applications. To address this issue, we introduce an online temporal smoothness technique and a low-pass filter to reduce the amplitude and frequency of the control actions. Fast Fourier Transform (FFT) is used to analyze policy performance in the frequency domain. Simulation results demonstrate the improvements achieved by the two proposed techniques.

📄 PDF Abstract BibTeX arXiv:2507.04346

Code (0)

등록된 구현이 없습니다.

Lyapunov Function-guided Reinforcement Learning for Flight Control

2025-10-26 · Yifei Li, Erik-Jan van Kampen 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 in…

Reinforcement Learning

Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control

2022-02-16 · Killian Dally, Erik-Jan van Kampen

Fault-tolerant flight control faces challenges, as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Stochastic Control of UAVs: An Optimal Tradeoff between Performance, Flight Smoothness and Control Effort

2024-09-16 · George Rapakoulias, Panagiotis Tsiotras

Safe and accurate control of unmanned aerial vehicles in the presence of winds is a challenging control problem due to the hard-to-model and highly stochastic nature of the disturbance forces acting upon the vehicle. To …

Regularizing Action Policies for Smooth Control with Reinforcement Learning

2020-12-11 · Siddharth Mysore, Bassel Mabsout, Renato Mancuso, Kate Saenko

A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies. This trend often presents itself in …

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Robust $\mathcal{H}_\infty$ Controller Design For INDI-Controlled Quadrotor Using Online Parameter Identification

2026-05-06 · Tom Aantjes, Till M. Blaha, Spilios Theodoulis, Ewoud J. J. Smeur arxiv

It has recently been shown that all physical parameters of an Incremental Nonlinear Dynamic Inversion (INDI) controller can be estimated onboard a multirotor within half a second, which is fast enough to do the full iden…