Tractable robust model predictive control with adaptive sliding mode for uncertain nonlinear systems

Shekoofeh Jafari Fesharaki, Farid Sheikholeslam, Marzieh Kamali and Ali Talebi

International Journal of Systems Science, 2020, vol. 51, issue 12, 2204-2216

Abstract: This paper proposes a tractable robust nonlinear model predictive control for continuous-time uncertain systems with stability guaranteed. The uncertainty is considered in parameters or additive form. First, a sampled-data model predictive control for the nominal system is designed to provide the desired performance. Then, an adaptive sliding mode control is designed to recover the nominal performance for the uncertain system. By merging sampled-data model predictive control and sliding mode control in-between samples, the effect of the uncertainty is reduced efficiently. The computational complexity of the proposed robust model predictive control is the same as for the model predictive control while asymptotic stability of the closed-loop system is achieved. The simulation results illustrate the effectiveness of the proposed approaches.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2020.1793230 (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:51:y:2020:i:12:p:2204-2216

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

DOI: 10.1080/00207721.2020.1793230

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 ().