A statistical learning strategy for closed-loop control of fluid flows
Guéniat, Florimond and Mathelin, Lionel and Yousuff Hussaini, M (2016) A statistical learning strategy for closed-loop control of fluid flows. Theoretical and Computational Fluid Dynamics, 30 (6). pp. 497-510. ISSN 0935-4964
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Abstract
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system’s dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz’63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.
| Item Type: | Article |
|---|---|
| Additional Information: | This is a post-peer-review, pre-copyedit version of an article published in Theoretical and Computational Fluid Dynamics. The final authenticated version is available online https://doi.org/10.1007/s00162-016-0392-y |
| Identification Number: | DOI 10.1007/s00162-016-0392-y |
| Dates: | Date Event 21 April 2016 Published Online 31 March 2016 Accepted |
| Uncontrolled Keywords: | Closed-loop control, Reinforcement learning, Machine learning |
| Subjects: | CAH07 - physical sciences > CAH07-04 - general, applied and forensic sciences > CAH07-04-01 - physical sciences (non-specific) CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific) |
| Divisions: | Faculty of Computing, Engineering and the Built Environment > College of Engineering |
| Depositing User: | Euan Scott |
| Date Deposited: | 18 Jan 2019 15:57 |
| Last Modified: | 20 Jun 2024 11:51 |
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/6871 |
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