Please use this identifier to cite or link to this item: https://doi.org/10.1002/rnc.1749
Title: Constraint-admissible sets for systems with soft constraints and their application in model predictive control
Authors: Wang, C. 
Ong, C.-J. 
Keywords: linear system with bounded disturbances
model predictive control
probabilistic constraint-admissible set
Issue Date: 25-Jul-2012
Source: Wang, C., Ong, C.-J. (2012-07-25). Constraint-admissible sets for systems with soft constraints and their application in model predictive control. International Journal of Robust and Nonlinear Control 22 (11) : 1229-1243. ScholarBank@NUS Repository. https://doi.org/10.1002/rnc.1749
Abstract: Constraint-admissible sets have been widely used in the study of control systems with hard constraints. This paper proposes a generalization of the maximal constraint-admissible set for constrained linear discrete-time systems to the case where soft or probabilistic constraints are present. Defined in the most obvious way, the maximal probabilistic constraint-admissible set is not invariant. An inner approximation of it is proposed which is invariant and has other nice properties. The application of this approximate set in a model predictive control framework with probabilistic constraints is discussed, including the feasibility and stability of the resulting closed-loop system. The effectiveness of the proposed approach is illustrated via numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.
Source Title: International Journal of Robust and Nonlinear Control
URI: http://scholarbank.nus.edu.sg/handle/10635/59785
ISSN: 10498923
DOI: 10.1002/rnc.1749
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