Advanced Data Methods for Improved Tiltrotor Test and Design

Informazioni relative al progetto

ADMITTED

ID dell’accordo di sovvenzione: 832003

DOI 10.3030/832003

Progetto chiuso

Data della firma CE 15 Febbraio 2019

Data di avvio 1 Febbraio 2019

Data di completamento 30 Novembre 2023

Finanziato da

• SOCIETAL CHALLENGES - Smart, Green And Integrated Transport

Costo totale € 1 718 330,00

Contributo UE € 1 718 330,00

Coordinato da

TXT E-TECH SRL
Italy

Risultati finali

Documents, reports (1)

Dissemination strategy and plan, Project website and media content

This deliverable provides the overall project dissemination strategy and plan as well as will ensure the public website at M3 for the project is up and running since the first month of the project, and will also populate it with the initial contents, including a one-pager leaflet for the early dissemination.

Pubblicazioni

Conference proceedings (3)

Model Structures and Fitting Criteria for System Identification with Neural Networks

Autori: Marco Forgione, Dario Piga
Pubblicato in: 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), 2020, Pagina/e 1-6, ISBN 978-1-7281-7386-3
Editore: IEEE
DOI: 10.1109/aict50176.2020.9368834

Efficient Calibration of Embedded MPC

Autori: Marco Forgione, Dario Piga, Alberto Bemporad
Pubblicato in: IFAC-PapersOnLine, Numero 53/2, 2020, Pagina/e 5189-5194, ISSN 2405-8963
Editore: Arxiv
DOI: 10.1016/j.ifacol.2020.12.1188

Building Digital Transformation to improve NGCTR design and simulation

Autori: Michele Sesana, Alessandro Pietro Bardelli
Pubblicato in: IOP Conference Series: Materials Science and Engineering, Numero 1024, 2021, Pagina/e 012103, ISSN 1757-899X
Editore: iopscience.iop.org
DOI: 10.1088/1757-899x/1024/1/012103

Peer reviewed articles (5)

Integrated Neural Networks for Nonlinear Continuous-Time System Identification

Autori: Bojan Mavkov, Marco Forgione, Dario Piga
Pubblicato in: IEEE Control Systems Letters, 2021, Pagina/e 1-1, ISSN 2475-1456
Editore: IEEE Control s\Systems Letters
DOI: 10.1109/lcsys.2020.2994806

Continuous-time system identification with neural networks: Model structures and fitting criteria

Autori: Marco Forgione, Dario Piga
Pubblicato in: European Journal of Control, Numero 59, 2021, Pagina/e 69-81, ISSN 0947-3580
Editore: Lavoisier
DOI: 10.1016/j.ejcon.2021.01.008

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification

Autori: Marco Forgione; Dario Piga
Pubblicato in: 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023, Numero Volume 56, Numero 2, 2023, 2023, Pagina/e 4082 - 4087, ISSN 2405-8963
Editore: Elsevier
DOI: 10.1016/j.ifacol.2023.10.1736

Deep learning with transfer functions: new applications in system identification

Autori: Dario Piga, Marco Forgione, Manas Mejari
Pubblicato in: IFAC-PapersOnLine, Numero 54/7, 2021, Pagina/e 415-420, ISSN 2405-8963
Editore: Elsevier
DOI: 10.1016/j.ifacol.2021.08.395

Learning dynamical systems from quantized observations: a Bayesian perspective

Autori: Dario Piga; Manas Mejari; Marco Forgione
Pubblicato in: IEEE Transactions on Automatic Control, Numero Volume 67, Numero 10, Oct. 2022, 2022, Pagina/e 5471 - 5478, ISSN 0018-9286
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tac.2021.3122385

Non-peer reviewed articles (1)

dynoNet : A neural network architecture for learning dynamical systems

Autori: Marco Forgione, Dario Piga
Pubblicato in: International Journal of Adaptive Control and Signal Processing, Numero 35/4, 2021, Pagina/e 612-626, ISSN 0890-6327
Editore: John Wiley & Sons Inc.
DOI: 10.1002/acs.3216