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Output feedback stochastic MPC with packet losses

Abstract:
The paper considers constrained linear systems with stochastic additive disturbances and noisy measurements transmitted over a lossy communication channel. We propose a model predictive control (MPC) law that minimizes a discounted cost subject to a discounted expectation constraint. Sensor data is assumed to be lost with known probability, and data losses are accounted for by expressing the predicted control policy as an affine function of future observations, which results in a convex optimal control problem. An online constrainttightening technique ensures recursive feasibility of the online optimization and satisfaction of the expectation constraint without bounds on the distributions of the noise and disturbance inputs. The cost evaluated along trajectories of the closed loop system is shown to be bounded by the optimal predicted cost. A numerical example is given to illustrate these results.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.ifacol.2020.12.506

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-2189-7876
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-0456-4124
Publisher:
Elsevier
Journal:
IFAC-PapersOnLine More from this journal
Volume:
53
Issue:
2
Pages:
7105-7110
Publication date:
2021-04-14
Acceptance date:
2020-02-27
Event title:
21st IFAC World Congress
Event location:
Berlin, Germany
Event website:
https://www.ifac2020.org/
Event start date:
2020-07-12
Event end date:
2020-07-17
DOI:
ISSN:
2405-8963
Language:
English
Keywords:
Pubs id:
1090535
Local pid:
pubs:1090535
Deposit date:
2020-04-06

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