Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2009.04.012
Title: Decomposition principle in model predictive control for linear systems with bounded disturbances
Authors: Sui, D.
Feng, L.
Hovd, M.
Ong, C.J. 
Keywords: Decomposition principle
Linear constrained systems with bounded disturbances
Model predictive control
Issue Date: Aug-2009
Source: Sui, D., Feng, L., Hovd, M., Ong, C.J. (2009-08). Decomposition principle in model predictive control for linear systems with bounded disturbances. Automatica 45 (8) : 1917-1922. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2009.04.012
Abstract: Considering a constrained linear system with bounded disturbances, this paper proposes a novel approach which aims at enlarging the domain of attraction by combining a set-based MPC approach with a decomposition principle. The idea of the paper is to extend the "pre-stabilizing" MPC, where the MPC control sequence is parameterized as perturbations to a given pre-stabilizing feedback gain, to the case where the pre-stabilizing feedback law is given as the linear combination of a set of feedback gains. This procedure leads to a relatively large terminal set and consequently a large domain of attraction even when using short prediction horizons. As time evolves, by minimizing the nominal performance index, the resulting controller reaches the desired optimal controller with a good asymptotic performance. Compared to the standard "pre-stabilizing" MPC, it combines the advantages of having a flexible choice of feedback gains, a large domain of attraction and a good asymptotic behavior. © 2009 Elsevier Ltd. All rights reserved.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/59840
ISSN: 00051098
DOI: 10.1016/j.automatica.2009.04.012
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