357
Views
4
CrossRef citations to date
0
Altmetric
Original Articles

Adaptive distributed unknown input observers for interconnected linear descriptor systems

&
Pages 182-189 | Received 22 Apr 2015, Accepted 29 Mar 2016, Published online: 19 Apr 2016
 

ABSTRACT

We propose an adaptive distributed algorithm for distributed observers of linear time-invariant descriptor systems. In the proposed algorithm, the interconnection gains of the resulting distributed unknown input observers are adjusted adaptively and the adaptation law is obtained by a Lyapunov-redesign approach. The scheme uses adaptive gains for each pairwise difference in the coupling term, which are adjusted in proportion to the pairwise differences of the state estimates. A special case where a single adaptive gain is used in each node to uniformly penalise all pairwise differences of the state estimates in the coupling term is also presented. Stability of the proposed schemes is proved and it is shown to be independent of the graph topology of the network. A numerical example is provided to illustrate the performance of the proposed adaptive distributed unknown input observers algorithm and to compare it with the non-interacting (local) unknown input observers.

Disclosure statement

No potential conflict of interest was reported by the authors.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,413.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.