Published March 2022
| Submitted + Published
Journal Article
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Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Abstract
We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances consistency, which measures the competitive ratio when predictions are accurate, and robustness, which bounds the competitive ratio when predictions are inaccurate.
Additional Information
© 2022 held by the owner/author(s). Received October 2021; revised December 2021; accepted January 2022. This work is supported by the National Science Foundation, under grants ECCS1931662, CCF 1637598, ECCS 1619352, CPS 1739355, AitF-1637598, CNS-1518941, PIMCO and Amazon Web Services. Tongxin Li and Ruixiao Yang contributed equally to the paper.Attached Files
Published - 3508038.pdf
Submitted - 2106.09659.pdf
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2106.09659.pdf
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Additional details
- Alternative title
- Robustness and Consistency in Linear Quadratic Control with Predictions
- Eprint ID
- 109906
- Resolver ID
- CaltechAUTHORS:20210716-225846876
- ECCS-1931662
- NSF
- CCF-1637598
- NSF
- ECCS-1619352
- NSF
- ECCS-1739355
- NSF
- CCF-1637598
- NSF
- CNS-1518941
- NSF
- PIMCO
- Amazon Web Services
- Created
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2021-07-16Created from EPrint's datestamp field
- Updated
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2022-08-02Created from EPrint's last_modified field