# Autonomous Blimp Control via H-infinity Robust Deep Residual Reinforcement Learning

arXiv: https://arxiv.org/abs/2303.13929 · HTML: https://mlsanity.com/paper/2303.13929/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Continual Uncertainty Learning for Robust Control of Nonlinear Systems with Multiple Heterogeneous Uncertainties](https://mlsanity.com/paper/2602.17174/) (2026)
- [Bi-Level Reinforcement Learning Control for an Underactuated Blimp via Center-of-Mass Reconfiguration](https://mlsanity.com/paper/2605.01289/) (2026)

## Abstract (arXiv)

Due to their superior energy efficiency, blimps may replace quadcopters for long-duration aerial tasks. However, designing a controller for blimps to handle complex dynamics, modeling errors, and disturbances remains an unsolved challenge. One recent work combines reinforcement learning (RL) and a PID controller to address this challenge and demonstrates its effectiveness in real-world experiments. In the current work, we build on that using an H-infinity robust controller to expand the stability margin and improve the RL agent's performance. Empirical analysis of different mixing methods reveals that the resulting H-infinity-RL controller outperforms the prior PID-RL combination and can handle more complex tasks involving intensive thrust vectoring. We provide our code as open-source at https://github.com/robot-perception-group/robust_deep_residual_blimp.