Fényes, Dániel and Németh, Balázs and Gáspár, Péter (2026) Learning-based LPV control for autonomous vehicles. In: 23rd IFAC World Congress, ifac2026.org, Busan, Republic of Korea. (In Press)
|
Text
IFAC1.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (739kB) | Preview |
Abstract
The paper presents a novel combination method for integrating an LPV-based controller with reinforcement learning. The main idea behind the combination is that the agent learns the scheduling parameters of the LPV observer to cope with the unmodeled or uncertain dynamics of the considered system. The RL aims to simultaneously minimize the error of the observer and the tracking performance of the controller. In this way, the exploration of the nonlinear system and the stability of the closed-loop system can be guaranteed. The proposed method is validated through two vehicle-oriented control problems, namely the trajectory tracking of ground vehicles and traction control of trains.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | T Technology / alkalmazott, műszaki tudományok > TL Motor vehicles. Aeronautics. Astronautics / járműtechnika, repülés, űrhajózás |
| Depositing User: | Dr Dániel Fényes |
| Date Deposited: | 23 Sep 2026 06:59 |
| Last Modified: | 23 Sep 2026 06:59 |
| URI: | https://real.mtak.hu/id/eprint/247095 |
Actions (login required)
 |
View Item |
