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Sliding-Mode Control for Systems With Mismatched Uncertainties via a Disturbance Observer

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Yang, Jun, Li, Shihua, Yu, Xinghuo·2013·IEEE Transactions on Industrial Electronics

Abstract

This paper develops a sliding-mode control (SMC) approach for systems with mismatched uncertainties via a nonlinear disturbance observer (DOB). By designing a novel sliding surface based on the disturbance estimation, a DOB-based SMC method is developed in this paper to counteract the mismatched disturbance. The newly proposed method exhibits the following two attractive features. First, the switching gain is only required to be designed greater than the bound of the disturbance estimation error rather than that of the disturbance; thus, the chattering problem is substantially alleviated. Second, the proposed method retains its nominal performance, which means the proposed method acts the same as the baseline sliding-mode controller in the absence of uncertainties. Simulation results of both the numerical and application examples show that the proposed method exhibits much better control performance than the baseline SMC and the integral SMC (I-SMC) methods, such as reduced chattering and nominal performance recovery.

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Metadata

Authors
Yang, J., Li, S., & Yu, X.
Venue
IEEE Transactions on Industrial Electronics
Published
2013-01
Volume
60
Issue
1
Pages
160-169
ISSN
  • 0278-0046 (ISSN-L)
  • 1557-9948
OpenAlex
W2010699465
Cited by
1,332
References
0
FWCI
84.38
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Sliding-Mode Control for Systems With Mismatched Uncertainties via a Disturbance Observer. https://app.cochat.ai/papers/10.1109/tie.2012.2183841
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Yang, J., Li, S., & Yu, X. (2013). Sliding-Mode Control for Systems With Mismatched Uncertainties via a Disturbance Observer. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2012.2183841

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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.