Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach

This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not ass...

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Main Authors: Yin, YanYan, Liu, Y., Teo, Kok Lay, Wang, S.
Format: Journal Article
Published: John Wiley & Sons Ltd. 2017
Online Access:http://hdl.handle.net/20.500.11937/54769
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author Yin, YanYan
Liu, Y.
Teo, Kok Lay
Wang, S.
author_facet Yin, YanYan
Liu, Y.
Teo, Kok Lay
Wang, S.
author_sort Yin, YanYan
building Curtin Institutional Repository
collection Online Access
description This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed-loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example.
first_indexed 2025-11-14T10:00:10Z
format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T10:00:10Z
publishDate 2017
publisher John Wiley & Sons Ltd.
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-547692018-01-03T08:34:05Z Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach Yin, YanYan Liu, Y. Teo, Kok Lay Wang, S. This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed-loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example. 2017 Journal Article http://hdl.handle.net/20.500.11937/54769 10.1002/rnc.3858 John Wiley & Sons Ltd. unknown
spellingShingle Yin, YanYan
Liu, Y.
Teo, Kok Lay
Wang, S.
Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach
title Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach
title_full Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach
title_fullStr Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach
title_full_unstemmed Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach
title_short Event-triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach
title_sort event-triggered probabilistic robust control of linear systems with input constrains: by scenario optimization approach
url http://hdl.handle.net/20.500.11937/54769