Network-based gain-scheduled control for preview path tracking of autonomous electric vehicles subject to communication delays
Zifan Gao,
Tao Wu,
Dawei Zhang and
Shuqian Zhu
International Journal of Systems Science, 2022, vol. 53, issue 12, 2549-2565
Abstract:
The preview path tracking problem of autonomous electric vehicles with event-triggered transmission and bounded communication delays is addressed using polytope modelling and asynchronous gain-scheduled control methods. By finding a polytope with as few vertices as possible to wrap the curve composed of all time-varying parameters, a new polytope modelling method is proposed to describe the vehicle lateral dynamics, which can not only reduce the modelling complexity greatly but also keep the modelling accuracy. Based on the polytope system model, a network-based gain-scheduled controller that operates asynchronously with the polytope system is constructed, where the scheduling parameters are driven by transmitted longitudinal velocity. By computing the deviation bounds of scheduling parameters and utilising the deviation-bound-dependent method, an asynchronous gain-scheduled control design method is presented. The feasibility and advantages of the proposed methods are shown by numerical verification.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2021.2005177 (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:53:y:2022:i:12:p:2549-2565
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20
DOI: 10.1080/00207721.2021.2005177
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 ().