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Stochastic tube MPC for LPV systems with probabilistic set inclusion conditions

Stochastic tube MPC for LPV systems with probabilistic set inclusion conditions
Stochastic tube MPC for LPV systems with probabilistic set inclusion conditions
The problem of controlling an LPV system subject to both hard and probabilistic constraints is considered. A necessary and sufficient condition for the inclusion of a polytope within another polytope, which is defined in terms of random variables, is given. This leads naturally to a tube MPC optimisation including linear probabilistic constraints. This can be solved approximately by considering a related problem obtained by sampling, which gives a mixed integer programming problem with a convex continuous relaxation. For fast sampling applications, we outline efficient approximate methods of solving the MIP via greedy constraint removal.
0191-2216
4783-4788
IEEE
Fleming, James
b59cb762-da45-43b1-b930-13dd9f26e148
Cannon, Mark
d2a52d25-9100-4a93-9bc7-8d10f4f3fa17
Kouvaritakis, Basil
ae3159e2-f89e-4907-89ec-3127049716ba
Fleming, James
b59cb762-da45-43b1-b930-13dd9f26e148
Cannon, Mark
d2a52d25-9100-4a93-9bc7-8d10f4f3fa17
Kouvaritakis, Basil
ae3159e2-f89e-4907-89ec-3127049716ba

Fleming, James, Cannon, Mark and Kouvaritakis, Basil (2015) Stochastic tube MPC for LPV systems with probabilistic set inclusion conditions. In 53rd IEEE Conference on Decision and Control. IEEE. pp. 4783-4788 . (doi:10.1109/CDC.2014.7040135).

Record type: Conference or Workshop Item (Paper)

Abstract

The problem of controlling an LPV system subject to both hard and probabilistic constraints is considered. A necessary and sufficient condition for the inclusion of a polytope within another polytope, which is defined in terms of random variables, is given. This leads naturally to a tube MPC optimisation including linear probabilistic constraints. This can be solved approximately by considering a related problem obtained by sampling, which gives a mixed integer programming problem with a convex continuous relaxation. For fast sampling applications, we outline efficient approximate methods of solving the MIP via greedy constraint removal.

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More information

e-pub ahead of print date: 2014
Published date: 12 February 2015
Venue - Dates: IEEE: 53rd Conference on Decision and Control, , Los Angeles, United States, 2014-12-15 - 2014-12-17

Identifiers

Local EPrints ID: 424457
URI: http://eprints.soton.ac.uk/id/eprint/424457
ISSN: 0191-2216
PURE UUID: 19e51755-c5aa-4f95-955a-9137979dccaa
ORCID for James Fleming: ORCID iD orcid.org/0000-0003-2936-4644

Catalogue record

Date deposited: 05 Oct 2018 11:37
Last modified: 09 Nov 2021 14:02

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Contributors

Author: James Fleming ORCID iD
Author: Mark Cannon
Author: Basil Kouvaritakis

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