Skip to main content

Robust Sliding Mode Observers for Large Scale Systems with Application to a Multimachine Power System

Mohamed, Mokhtar, Yan, Xinggang, Spurgeon, Sarah K., Jiang, Bin (2016) Robust Sliding Mode Observers for Large Scale Systems with Application to a Multimachine Power System. IET Control Theory and Applications, 11 (8). pp. 1307-1315. ISSN 1751-8644. (doi:10.1049/iet-cta.2016.1204) (KAR id:58513)

PDF Author's Accepted Manuscript
Language: English
Download (259kB) Preview
[thumbnail of iet_paper_yan.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL


In this paper, a class of interconnected systems with structured and unstructured uncertainties is considered where the known interconnections and uncertain interconnections are nonlinear. The bounds on the uncertainties are employed in the observer design to enhance the robustness when the structure of the uncertainties is available for design. Under the condition that the structure distribution matrices of the uncertainties are known, a robust sliding mode observer is designed and a set of sufficient conditions is developed to guarantee that the error dynamics are asymptotically stable. In the case that the structure of uncertainties is unknown, an ultimately bounded approximate observer is developed to estimate the system states using sliding mode techniques.

The results obtained are applied to a multimachine power system, and simulation for a two machine power system is presented to demonstrate the feasibility and effectiveness of the developed methods.

Item Type: Article
DOI/Identification number: 10.1049/iet-cta.2016.1204
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Xinggang Yan
Date Deposited: 10 Nov 2016 11:05 UTC
Last Modified: 16 Feb 2021 13:39 UTC
Resource URI: (The current URI for this page, for reference purposes)
Yan, Xinggang:
  • Depositors only (login required):


Downloads per month over past year