Aperiodic speed tracking control of unmanned sightseeing vehicles based on hierarchical reinforcement learning

Haiying Wan, Yingqiang Tian, Chenyang Wang, Xiaoli Luan, Hamid Reza Karimi and Fei Liu

International Journal of Systems Science, 2025, vol. 56, issue 10, 2343-2356

Abstract: This paper proposes an aperiodic control approach based on hierarchical reinforcement learning for the longitudinal speed tracking control of unmanned sightseeing vehicles in scenic areas. The method develops the MAXQ hierarchical framework to decompose the speed tracking task into a series of subtasks, including learning the control inputs and the triggering instants. This allows each subtask to focus on a specific local issue, independently learning and optimising, making the learning process simpler and more efficient. In the learning of triggering instants, an event-triggered strategy is incorporated, which greatly reduces the controller update rate, thereby reducing communication and resources occupation. In the learning of control inputs, the reward function not only considers tracking performance, but also considers both inter-vehicle distance and acceleration change rate, which ensures tracking accuracy while enhancing driving safety and comfort. Finally, the efficacy of the proposed method is validated through a simulation study.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2024.2447359 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:56:y:2025:i:10:p:2343-2356

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2024.2447359

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().