Challenges and opportunities in developing INN-based control systems for modular drones
Publication date
2025-05-27
Document type
Konferenzbeitrag
Author
Ludwigs, Robert
Kampker, Achim
Organisational unit
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2025
First page
69
Last page
78
Part of the university bibliography
✅
Language
English
Abstract
As drone technology evolves, modular drones are increasingly central, offering rapid adaptability through the interchange of sensors, motors, and structural battery modules. However, this flexibility also introduces complex control challenges that traditional Proportional-Integral-Derivative (PID) controllers often struggle to address, particularly under dynamic reconfigurations and nonlinear responses. In this paper, we propose a novel approach integrating Invertible Neural Networks (INNs) and Reinforcement Learning (RL) to enhance adaptability and effectiveness in modular drone control. INNs facilitate precise, reversible command mapping via bijective transformations, ensuring robust handling of changing drone weight, geometry, and functionality. When combined with RL, these networks further enable real-time optimization of flight performance, dynamically responding to shifts in operational conditions. We outline a comprehensive research agenda employing the PX4 simulation framework to benchmark INN- and RL-based methods against standard PID controllers, focusing on improved response times, reduced error rates, and better system resilience. The anticipated findings aim to substantiate the potential of these advanced control systems – particularly in conjunction with emerging structural battery designs – to significantly expand the capabilities and operational scope of next-generation unmanned aerial vehicle (UAVs) in real-world applications.
Version
Published version
Access right on openHSU
Open access