A novel adaptive control approach for nonlinear strict-feedback systems using nonlinearly parameterised fuzzy approximators

Ping Li and Guang-Hong Yang

International Journal of Systems Science, 2011, vol. 42, issue 3, 517-527

Abstract: In this article, the problem of output tracking of perturbed nonlinear strict-feedback systems is addressed and a novel adaptive fuzzy control scheme is proposed. The considered systems are with unknown nonlinearities, so an adaptive fuzzy approximation approach is embedded into a backstepping procedure to get the proposed controller. However, unlike the exiting results, approximators used in this article are not linearly parameterised. Using nonlinearly parameterised adaptive fuzzy approximators, the controller can be obtained without the restriction that fuzzy basis functions of the approximators must be well defined. By managing to adapt the norm of on-line parameter vectors in the control design, the computation burden is largely reduced. The proposed controller can guarantee the stability and desired tracking performance of the closed-loop system. An example is included to demonstrate the effectiveness of the control scheme.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721003624576 (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:42:y:2011:i:3:p:517-527

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

DOI: 10.1080/00207721003624576

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 ().