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Suboptimal model predictive control (feasibility implies stability)

Scokaert, P.O.M., Mayne, D.Q., Rawlings, J.B.·1999·IEEE Transactions on Automatic Control

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.

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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

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CoChat. (2026). Citation check: Suboptimal model predictive control (feasibility implies stability). https://app.cochat.ai/papers/10.1109/9.751369
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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

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