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Robust control for dynamical systems with non-Gaussian noise via formal abstractions

Abstract:

Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel controller synthesis method that does not rely on any explicit represe...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1613/jair.1.14253

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Trinity College
Role:
Author
ORCID:
0000-0003-4137-8862
Publisher:
AI Access Foundation
Journal:
Journal of Artificial Intelligence Research More from this journal
Volume:
76
Pages:
341-391
Publication date:
2023-01-21
Acceptance date:
2022-12-24
DOI:
EISSN:
1943-5037
ISSN:
1076-9757
Language:
English
Keywords:
Pubs id:
1324946
Local pid:
pubs:1324946
Deposit date:
2023-01-23

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