The DOI matches its Crossref record.
Robust Constrained Control for MIMO Nonlinear Systems Based on Disturbance Observer
Abstract
In this note, a robust constrained control scheme is proposed for multi-input and multi-output (MIMO) cascade nonlinear systems with unknown external disturbance and input saturation. To efficiently observe unknown external disturbance, a novel sliding mode disturbance observer (SMDO) with finite time convergence performance is presented. A robust constrained control scheme is developed for MIMO nonlinear systems based on backstepping control techniques and the disturbance estimate of the SMDO. With the first-order sliding mode differentiator introduced to compute the derivatives of virtual control laws, the computational burden in backstepping control is reduced for a MIMO cascade nonlinear system. Numerical simulation results are presented to show the effectiveness of the proposed robust constrained control scheme.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Cui, R., Yang, C., Li, Y., & Sharma, S. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2645699 · Cited by 538
Sun, K., Qiu, J., Karimi, H. R., & Fu, Y. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2979129 · Cited by 334
Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems
Liu, Y.-J., Lu, S., Li, D., & Tong, S. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2633007 · Cited by 284
Li, Y., & Tong, S. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2574913 · Cited by 264
Niu, B., & Li, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2690465 · Cited by 185
Metadata
- Authors
- Chen, M., Shi, P., & Lim, C.-C.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2015-12
- Volume
- 60
- Issue
- 12
- Pages
- 3281-3286
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W2185249669
- Cited by
- 273
- References
- 0
- FWCI
- 27.22
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Robust Constrained Control for MIMO Nonlinear Systems Based on Disturbance Observer. https://app.cochat.ai/papers/10.1109/tac.2015.2450891
- The paper
- Chen, M., Shi, P., & Lim, C.-C. (2015). Robust Constrained Control for MIMO Nonlinear Systems Based on Disturbance Observer. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2015.2450891
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.