Data-driven optimal control of switched linear autonomous systems
Chi Zhang,
Minggang Gan and
Jingang Zhao
International Journal of Systems Science, 2019, vol. 50, issue 6, 1275-1289
Abstract:
In this paper, a novel data-driven optimal control approach of switching times is proposed for unknown continuous-time switched linear autonomous systems with a finite-horizon cost function and a prescribed switching sequence. No a priori knowledge on the system dynamics is required in this approach. First, some formulas based on the Taylor expansion are deduced to estimate the derivatives of a cost function with respect to the switching times using system state data. Then, a data-driven optimal control approach based on the gradient decent algorithm is designed, taking advantage of the derivatives to approximate the optimal switching times. Moreover, the estimation errors are analysed and proven to be bounded. Finally, simulation examples are illustrated to validate the effectiveness of the approach.
Date: 2019
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2019.1598512 (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:50:y:2019:i:6:p:1275-1289
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20
DOI: 10.1080/00207721.2019.1598512
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 ().