Optimisation of control and learning actions for a repetitive-control system based on Takagi–Sugeno fuzzy model

Manli Zhang, Min Wu, Luefeng Chen, Shengnan Tian and Jinhua She

International Journal of Systems Science, 2020, vol. 51, issue 15, 3030-3043

Abstract: This paper deals with the problem of designing a two-dimensional (2D) modified repetitive-control system based on a Takagi–Sugeno (T-S) fuzzy model to achieve high tracking performance for a nonlinear plant. First, a nonlinear plant is represented by a T-S fuzzy model, and a modified repetitive controller with two repetitive loops is used to increases design flexibility. Next, a continuous-discrete 2D model is established to make use of the 2D characteristics in the modified repetitive-control system. Then, a sufficient stability condition is derived in terms of linear matrix inequalities. Three parameters are used to balance continuous control and discrete learning actions: one in a repetitive loop and two in a Lyapunov–Krasovskii functional. A particle swarm optimisation algorithm yields optimal parameters and the gains of the modified repetitive and state-feedback controllers. Finally, simulation and comparison results demonstrate the effectiveness of our method.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2020.1807651 (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:15:p:3030-3043

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

DOI: 10.1080/00207721.2020.1807651

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 ().