- Abstract:
-
In this paper, we propose a distributed stochastic model predictive control (DSMPC) algorithm for a team of linear subsystems sharing coupled probabilistic constraints. Each subsystem is subject to both parameter uncertainty and stochastic disturbances. To handle the probabilistic constraints, we first decompose the state trajectory into a nominal part and an uncertain part. The latter one is further divided into two parts: one is bounded by probabilistic tubes that are calculated offl...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted Manuscript
- Files:
-
-
(pdf, 474.9KB)
-
- Publisher copy:
- 10.1109/ChiCC.2016.7554022
-
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- Grant:
- 61225015; 61105092; 61321002
- Publisher:
- Technical Committee on Control Theory Publisher's website
- Pages:
- 4312-4317
- Publication date:
- 2016-08-29
- DOI:
- EISSN:
-
1934-1768
- URN:
-
uuid:f18a045e-09c8-427b-b01b-29d07fa575ec
- Source identifiers:
-
629612
- Local pid:
- pubs:629612
- ISBN:
- 978-9-8815-6391-0
- Copyright holder:
- IEEE
- Copyright date:
- 2016
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from IEEE at: http://dx.doi.org/10.1109/ChiCC.2016.7554022
Conference
Distributed stochastic MPC of linear systems with parameter uncertainty and disturbances
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+ National Natural Science
Foundation of China
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