Abstract
This article introduces a scenario optimization framework for reliability-based design given measurements of the uncertain parameters. In contrast to traditional methods, scenario optimization makes direct use of the available data thereby eliminating the need for assuming a distribution class and estimating its hyper-parameters. Scenario theory provides formal bounds on the probabilistic performance of a design decision and certifies the system ability to comply with various requirements for future/unseen observations. This probabilistic certificate of correctness is non-asymptotic and distribution-free. Furthermore, chance-constrained optimization techniques are used to detect and eliminate the effects of outliers in the resulting optimal design. The proposed framework is exemplified on a benchmark robust control challenge problem having conflicting design objectives.
Original language | English |
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Title of host publication | Dynamic Systems and Control Conference |
Number of pages | 8 |
ISBN (Electronic) | 978-0-7918-5915-5 |
DOIs | |
Publication status | Published - 26 Nov 2019 |
Event | ASME 2019 Dynamic Systems and Control Conference - Park City, Utah, United States Duration: 8 Oct 2019 → 11 Oct 2019 |
Conference
Conference | ASME 2019 Dynamic Systems and Control Conference |
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Country/Territory | United States |
City | Utah |
Period | 8/10/19 → 11/10/19 |
Keywords
- reliability-based optimization
- scenario theory
- controller design
- probability of failure
- oulier
- Outlier
- Reliability-based optimization
- Scenario theory
- Controller design
- Probability of failure