Adaptive control with optimal tracking performance
Sheng-Ping Li
International Journal of Systems Science, 2018, vol. 49, issue 3, 496-510
Abstract:
This paper provides a way to optimise the steady-state tracking performance of the adaptive control system in the presence of unknown external disturbances. A-priori knowledge of the dynamic model of the reference signal to be tracked is not completely known. Especially, the updatable non-empty admissible model set, which is consistent to the a-priori knowledge of the plant parameter and the online measurements, is computed. Treating the overall system performance as the criteria, the nominal model is optimally chosen within the admissible model set. The optimal nominal model is subsequently used to synthesise the optimal closed-loop controller that minimises the steady-state absolute value of the tracking error. Combining the above two aspects, an optimal adaptive control scheme is proposed. Because of the consistency of the identification criteria and control object, the adaptive control scheme proposed in this paper can achieve the overall optimal steady-state tracking performance, and the effect of the interplay between the identification and control of the adaptive system can be handled effectively. In addition, the computable optimal performance is also provided.
Date: 2018
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2017.1415390 (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:49:y:2018:i:3:p:496-510
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20
DOI: 10.1080/00207721.2017.1415390
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 ().