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• W1539283075 abstract "The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to explicitly deal with model uncertainty. In this paper, the authors 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." @default.
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• W1539283075 date "2005-08-25" @default.
• W1539283075 modified "2023-10-06" @default.
• W1539283075 title "Robust constrained model predictive control using linear matrix inequalities" @default.
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• W1539283075 doi "https://doi.org/10.1109/acc.1994.751775" @default.
• W1539283075 hasPublicationYear "2005" @default.
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