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Constrained model predictive control: Stability and optimality
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“Model predictive control (MPC) [12], or receding horizon control [13], is an optimization-based control addressing optimal tracking control performance and input and state constraints, such as saturation of control inputs and physical limits of workspace and speed.”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [12]
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Metadata
- 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.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2000-06
- Volume
- 36
- Issue
- 6
- Pages
- 789-814
- ISSN
- 0005-1098 (ISSN-L)
- 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
- 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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