Paper Info
Title
Linear systems with chance constraints: Constraint-admissible set and applications in predictive control
Abstract
Maximal constraint-admissible sets have been widely used in the study of linear systems with hard constraints. This paper proposes a generalization of the maximal constraint-admissible set to the case where chance or probabilistic constraints are present in a linear system. Properties of the probabilistic constraint-admissible set are discussed and it is shown that the maximal chance constraint-admissible set is not time invariant. An inner approximation to the maximal set is then proposed to ensure its invariance property. This approximate set is then applied in the design of a model predictive controller for a linear system with additive disturbances and chance constraints. Feasibility and stability of the resultant closed-loop system are discussed.
Year
DOI
Venue
2009
10.1109/CDC.2009.5400339
CDC
Keywords
Field
DocType
chance constraints,control system synthesis,model predictive controller,constraint theory,closed-loop system,probabilistic constraints,constraint-admissible set,linear systems,stability,closed loop systems,probability,predictive control,predictive models,probabilistic logic,control systems,linear system
LTI system theory,Control theory,Mathematical optimization,Maximal set,Invariant (physics),Linear system,Control theory,Computer science,Model predictive control,Admissible set,Probabilistic logic
Conference
ISSN
ISBN
Citations 
0191-2216 E-ISBN : 978-1-4244-3872-3
978-1-4244-3872-3
0
PageRank 
References 
Authors
0.34
16
3
Name
Order
Citations
PageRank
Chen Wang1142.03
Chong-Jin Ong271656.26
Melvyn Sim31909117.68