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Sliding mode observers: a survey

Spurgeon, Sarah K.·2008·International Journal of Systems Science

Abstract

Sliding mode observers have unique properties, in that the ability to generate a sliding motion on the error between the measured plant output and the output of the observer ensures that a sliding mode observer produces a set of state estimates that are precisely commensurate with the actual output of the plant. It is also the case that analysis of the average value of the applied observer injection signal, the so-called equivalent injection signal, contains useful information about the mismatch between the model used to define the observer and the actual plant. These unique properties, coupled with the fact that the discontinuous injection signals which were perceived as problematic for many control applications have no disadvantages for software-based observer frameworks, have generated a ground swell of interest in sliding mode observer methods in recent years. This article presents an overview of both linear and non-linear sliding mode observer paradigms. The use of the equivalent injection signal in problems relating to fault detection and condition monitoring is demonstrated. A number of application specific results are also described. The literature in the area is presented and qualified in the context of continuing developments in the broad areas of the theory and application of sliding mode observers.

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Metadata

Authors
Spurgeon, S. K.
Venue
International Journal of Systems Science
Published
2008-08
Volume
39
Issue
8
Pages
751-764
ISSN
  • 0020-7721 (ISSN-L)
  • 1464-5319
OpenAlex
W1982617281
Cited by
563
References
0
FWCI
45.66
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Sliding mode observers: a survey. https://app.cochat.ai/papers/10.1080/00207720701847638
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Spurgeon, S. K. (2008). Sliding mode observers: a survey. International Journal of Systems Science. https://doi.org/10.1080/00207720701847638

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.