The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Tutorial overview of model predictive control
Abstract
The paper provides a reasonably accessible and self-contained tutorial exposition on model predictive control (MPC). It is aimed at readers with control expertise, particularly practitioners, who wish to broaden their perspective in the MPC area of control technology. We introduce the concepts, provide a framework in which the critical issues can be expressed and analyzed, and point out how MPC allows practitioners to address the trade-offs that must be considered in implementing a control technology.
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
Cites
Constrained model predictive control: Stability and optimality
Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M. (2000) · Automatica · doi:10.1016/s0005-1098(99)00214-9 · Cited by 8,736
Self-citation
Cited by
Attacks against process control systems: risk assessment, detection, and response
Cárdenas, A. A., Amin, S., Lin, Z.-S., Huang, Y.-L., Huang, C.-Y., & Sastry, S. (2011) · Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security · doi:10.1145/1966913.1966959 · Cited by 699
Hu, Z., Gao, Y., Sun, L., Mae, M., & Imaizumi, T. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123652 · Cited by 52
Model Predictive Control for Finite Input Systems using the D-Wave Quantum Annealer
Inoue, D., & Yoshida, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-58081-9 · Cited by 39
Fang, Y., & Armaou, A. (2015) · 2015 American Control Conference (ACC) · doi:10.1109/acc.2015.7172221 · Cited by 14
Metadata
- Venue
- IEEE Control Systems
- Published
- 2000-06
- Volume
- 20
- Issue
- 3
- Pages
- 38-52
- ISSN
- 1066-033X (ISSN-L)
- 1941-000X
- OpenAlex
- W2002219970
- Cited by
- 1,108
- References
- 1
- FWCI
- 28.73
- 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: Tutorial overview of model predictive control. https://app.cochat.ai/papers/10.1109/37.845037
- The paper
- Tutorial overview of model predictive control. (2000). IEEE Control Systems. https://doi.org/10.1109/37.845037
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.
