Robust constrained model predictive control using linear matrix inequalities

Mayuresh V. Kothare, V. Balakrishnan, Manfred Morari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations


The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to explicitly deal with model uncertainty. In this paper, we address the robustness issue in MPC by directly incorporating the description of plant uncertainty in the MPC problem formulation. The plant uncertainty is expressed in the time-domain by allowing the state-space matrices of the discrete-time plant to be arbitrarily time-varying and belonging to a polytope. The existence of a feedback control law minimizing an upper bound on the infinite horizon objective function and satisfying the input and output constraints is reduced to a convex optimization over linear matrix inequalities (LMIs). It is shown that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherAmerican Automatic Control Council
Number of pages5
StatePublished - 1994
Externally publishedYes
EventProceedings of the 1994 American Control Conference. Part 1 (of 3) - Baltimore, MD, USA
Duration: Jun 29 1994Jul 1 1994


OtherProceedings of the 1994 American Control Conference. Part 1 (of 3)
CityBaltimore, MD, USA

ASJC Scopus subject areas

  • Control and Systems Engineering


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