The DOI matches its Crossref record.
Suboptimal model predictive control (feasibility implies stability)
Abstract
Practical difficulties involved in implementing stabilizing model predictive control laws for nonlinear systems are well known. Stabilizing formulations of the method normally rely on the assumption that global and exact solutions of nonconvex, nonlinear optimization problems are possible in limited computational time. In the paper, we first establish conditions under which suboptimal model predictive control (MPC) controllers are stabilizing; the conditions are mild holding out the hope that many existing controllers remain stabilizing even if optimality is lost. Second, we present and analyze two suboptimal MPC schemes that are guaranteed to be stabilizing, provided an initial feasible solution is available and for which the computational requirements are more reasonable.
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
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-citationRao, C. V., Rawlings, J. B., & Mayne, D. Q. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2002.808470 · Cited by 894
Self-citationThe advanced-step NMPC controller: Optimality, stability and robustness
Zavala, V. M., & Biegler, L. T. (2009) · Automatica · doi:10.1016/j.automatica.2008.06.011 · Cited by 417
Infinite Horizon Optimal Control
Grüne, L., & Pannek, J. (2011) · Communications and Control Engineering · doi:10.1007/978-0-85729-501-9_4 · Cited by 5
Metadata
- Authors
- Scokaert, P. O. M., Mayne, D. Q., & Rawlings, J. B.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1999-03
- Volume
- 44
- Issue
- 3
- Pages
- 648-654
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2102737570
- Cited by
- 614
- References
- 0
- FWCI
- 21.64
- 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: Suboptimal model predictive control (feasibility implies stability). https://app.cochat.ai/papers/10.1109/9.751369
- The paper
- Scokaert, P. O. M., Mayne, D. Q., & Rawlings, J. B. (1999). Suboptimal model predictive control (feasibility implies stability). IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.751369
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.