Cited by 8,736Advanced Control Systems OptimizationAdvanced Optimization Algorithms ResearchStability and Control of Uncertain Systems

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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

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“While this method is limited to quadratic costs, model predictive control (MPC) offers additional ﬂexibility by solving a receding horizon optimal control problem that can accommodate complex costs, constraints, and dynamics [2].”
Method

Authors: Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M.
Affiliations: Imperial College London (GB)
University of Wisconsin–Madison (US)
Orange (France) (FR)
Corresponding author: Mayne, D.Q.
ORCID: Rawlings, J.B.
Rao, C.V.
Venue: Automatica
Publisher: Elsevier BV
Published: 2000-06
Volume: 36
Issue: 6
Pages: 789-814
ISSN: 0005-1098 (ISSN-L)
DOI: 10.1016/s0005-1098(99)00214-9
OpenAlex: W1978956894
Cited by: 8,736
References: 127
Citations by year: 2026: 406 · 2025: 372 · 2024: 336 · 2023: 400 · 2022: 386 · 2021: 424 · 2020: 487 · 2019: 448 · 2018: 426 · 2017: 404 · 2016: 412 · 2015: 400 · 2014: 455 · 2013: 397 · 2012: 380
FWCI: 231.55
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Automatica (publishedVersion)
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
Keywords: constrained model predictive control, stability analysis, optimality, finite-horizon optimal control, infinite horizon optimal control, nonlinear model predictive control, linear model predictive control, hard constraints, petrochemical process control, nonlinear control

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Constrained model predictive control: Stability and optimality

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