Print Email Facebook Twitter High-Confidence Data-Driven Ambiguity Sets for Time-Varying Linear Systems Title High-Confidence Data-Driven Ambiguity Sets for Time-Varying Linear Systems Author Boskos, D. (TU Delft Team Dimitris Boskos) Cortes, Jorge (University of California) Martinez, Sonia (University of California) Date 2024 Abstract This paper builds Wasserstein ambiguity sets for the unknown probability distribution of dynamic random variables leveraging noisy partial-state observations. The constructed ambiguity sets contain the true distribution of the data with quantifiable probability and can be exploited to formulate robust stochastic optimization problems with out-of-sample guarantees. We assume the random variable evolves in discrete time under uncertain initial conditions and dynamics, and that noisy partial measurements are available. All random elements have unknown probability distributions and we make inferences about the distribution of the state vector using several output samples from multiple realizations of the process. To this end, we leverage an observer to estimate the state of each independent realization and exploit the outcome to construct the ambiguity sets. We illustrate our results in an economic dispatch problem involving distributed energy resources over which the scheduler has no direct control. Subject AerodynamicsDistributional uncertaintyestimationlinear system observersNoise measurementOptimizationPower system dynamicsProbability distributionRandom variablesstochastic systemsUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:5501c983-6dd4-46a7-81aa-be9839655272 DOI https://doi.org/10.1109/TAC.2023.3273815 Embargo date 2023-11-08 ISSN 0018-9286 Source IEEE Transactions on Automatic Control, 69 (2), 797-812 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2024 D. Boskos, Jorge Cortes, Sonia Martinez Files PDF High-Confidence_Data-Driv ... ystems.pdf 1.51 MB Close viewer /islandora/object/uuid:5501c983-6dd4-46a7-81aa-be9839655272/datastream/OBJ/view