Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Anna Scampicchio"'
Publikováno v:
Neurocomputing, 545
Estimating a set of orthogonal functions from a finite set of noisy data plays a crucial role in several areas such as imaging, dictionary learning and compressed sensing. The problem turns out especially hard due to its intrinsic non-convexity. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4b9064d2c1502dff6c8e7b120667b1d
Autor:
Anna Scampicchio, Andrea Iannelli
Publikováno v:
2022 American Control Conference (ACC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcf0deb594565587813ee628ab59859c
https://doi.org/10.23919/acc53348.2022.9867600
https://doi.org/10.23919/acc53348.2022.9867600
Autor:
Elena Arcari, Maria Vittoria Minniti, Anna Scampicchio, Andrea Carron, Farbod Farshidian, Marco Hutter, Melanie N. Zeilinger
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29e4812472ee77e381607397c3bee0cb
Publikováno v:
2021 60th IEEE Conference on Decision and Control (CDC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67f2e897b6585511a8ccf80909b08175
https://doi.org/10.1109/cdc45484.2021.9683483
https://doi.org/10.1109/cdc45484.2021.9683483
Autor:
Gianluigi Pillonetto, Anna Scampicchio
Publikováno v:
CDC
In this paper, we propose some new convex strategies for robust optimal control. In particular, we treat the problem of designing finite-horizon linear quadratic regulator (LQR) for uncertain discrete-time systems focusing on minimax strategies. A ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53119293cd324b4b2fcc29e7a80b2fd9
https://doi.org/10.1109/cdc42340.2020.9304314
https://doi.org/10.1109/cdc42340.2020.9304314
Linear Quadratic Regulator (LQR) design is one of the most classical optimal control problems, whose well-known solution is an input sequence expressed as a state-feedback. In this work, finite-horizon and discrete-time LQR is solved under stability
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ce45d7cd83c6b41b72ada1aebbb98c5
http://arxiv.org/abs/2001.05795
http://arxiv.org/abs/2001.05795
The solution of classic discrete-time, finite-horizon linear quadratic regulator (LQR) problem is well known in literature. By casting the solution to be a static state-feedback, we propose a new method that trades off low LQR objective value with cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1651606afd3e515d5626dc781c8d5aa
http://hdl.handle.net/11577/3389528
http://hdl.handle.net/11577/3389528
Publikováno v:
CDC
Recent contributions have investigated the use of regularization in linear system identification. In particular, regularizing high-order FIR models to enforce stability while controlling complexity and regularity of the impulse response provides stat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d4f0adb9f515745b055a9015ca080af
http://hdl.handle.net/11577/3332841
http://hdl.handle.net/11577/3332841