Zobrazeno 1 - 10
of 22 982
pro vyhledávání: '"Bemporad, A"'
Autor:
Avrutin, Eugene M., author, Bemporad, Elissa, author
Publikováno v:
Pogroms : A Documentary History, 2021, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780190060084.003.0001
Autor:
Bemporad, Elissa, author
Publikováno v:
Pogroms : A Documentary History, 2021, ill.
Externí odkaz:
https://doi.org/10.1093/oso/9780190060084.003.0008
Autor:
Kieval, Hillel1 (AUTHOR)
Publikováno v:
History: Reviews of New Books. May2023, Vol. 51 Issue 3, p68-70. 3p.
Dynamical models identified from data are frequently employed in control system design. However, decoupling system identification from controller synthesis can result in situations where no suitable controller exists after a model has been identified
Externí odkaz:
http://arxiv.org/abs/2411.18166
Autor:
JURIČ, STEPHANO
Publikováno v:
Angelicum, 2017 Jan 01. 94(1), 9-16.
Externí odkaz:
https://www.jstor.org/stable/26392612
Autor:
Bellezza, Simone Attilio
Publikováno v:
RICERCHE SLAVISTICHE. NUOVA SERIE / RICERCHE SLAVISTICHE. NEW SERIES. 4(64):341-344
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1134580
Autor:
Trachtenberg, Barry
Publikováno v:
Slavic Review, 2021 Jul 01. 80(2), 430-431.
Externí odkaz:
https://www.jstor.org/stable/27059681
We present EGN, a stochastic second-order optimization algorithm that combines the generalized Gauss-Newton (GN) Hessian approximation with low-rank linear algebra to compute the descent direction. Leveraging the Duncan-Guttman matrix identity, the p
Externí odkaz:
http://arxiv.org/abs/2405.14402
Autor:
Badalamenti, Filippo, Mulagaleti, Sampath Kumar, Bemporad, Alberto, Houska, Boris, Villanueva, Mario Eduardo
This paper proposes a novel tube-based Model Predictive Control (MPC) framework for tracking varying setpoint references with linear systems subject to additive and multiplicative uncertainties. The MPC controllers designed using this framework exhib
Externí odkaz:
http://arxiv.org/abs/2405.03629
This work considers the problem of optimal lane changing in a structured multi-agent road environment. A novel motion planning algorithm that can capture long-horizon dependencies as well as short-horizon dynamics is presented. Pivotal to our approac
Externí odkaz:
http://arxiv.org/abs/2405.02979