Marco Gallieri
Marco Gallieri
Senior Data Scientist, DXT Commodities. Former F1 Engineer, Cambridge PhD.
Verified email at - Homepage
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Cited by
assoMPC: Smart regulation of over-actuated systems
M Gallieri, JM Maciejowski
2012 American Control Conference (ACC), 1217-1222, 2012
Nais-net: Stable deep networks from non-autonomous differential equations
M Ciccone, M Gallieri, J Masci, C Osendorfer, F Gomez
Advances in Neural Information Processing Systems 31, 2018
Snode: Spectral discretization of neural odes for system identification
A Quaglino, M Gallieri, J Masci, J Koutník
arXiv preprint arXiv:1906.07038, 2019
Terminal spacecraft rendezvous and capture with LASSO model predictive control
EN Hartley, M Gallieri, JM Maciejowski
International Journal of Control 86 (11), 2104-2113, 2013
Infinite-horizon differentiable model predictive control
S East, M Gallieri, J Masci, J Koutnik, M Cannon
arXiv preprint arXiv:2001.02244, 2020
Triangular formation control using range measurements: An application to marine robotic vehicles
JM Soares, AP Aguiar, AM Pascoal, M Gallieri
IFAC Proceedings Volumes 45 (5), 112-117, 2012
Objectively Assessing Intraoperative Arthroscopic Skills Performance and the Transfer of Simulation Training in Knee Arthroscopy: A Randomized Controlled Trial
PG Roberts, M Gallieri, C Hargrove, J Rees
Journal of Arthroscopic and Related Surgery 35 (4), 1197-1209, 2019
Fault detection and prognosis methods for a monitoring system of rotating electrical machines
C Ciandrini, M Gallieri, A Giantomassi, G Ippoliti, S Longhi
2010 IEEE International Symposium on Industrial Electronics, 2085-2090, 2010
Lasso-MPC–Predictive Control with l1-Regularised Least Squares
M Gallieri
Springer, 2016
Stabilising terminal cost and terminal controller for ℓasso-MPC: enhanced optimality and region of attraction
M Gallieri, JM Maciejowski
2013 European Control Conference (ECC), 524-529, 2013
Safe Interactive Model-Based Learning
M Gallieri, SSM Salehian, NE Toklu, A Quaglino, J Masci, J Koutník, ..., 2019
Neural lyapunov model predictive control
M Mittal, M Gallieri, A Quaglino, SSM Salehian, J Koutník
Accelerating neural odes with spectral elements
A Quaglino, M Gallieri, J Masci, J Koutnık
arXiv preprint arXiv:1906.07038, 2019
On the adaptation of recurrent neural networks for system identification
M Forgione, A Muni, D Piga, M Gallieri
Automatica 155, 111092, 2023
Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems
S Pozzoli, M Gallieri, R Scattolini
IFAC-PapersOnLine 53 (2), 5171-5176, 2020
Real-time classification from short event-camera streams using input-filtering neural ODEs
G Giannone, A Anoosheh, A Quaglino, P D'Oro, M Gallieri, J Masci
arXiv preprint arXiv:2004.03156, 2020
Neural lyapunov model predictive control: Learning safe global controllers from sub-optimal examples
M Mittal, M Gallieri, A Quaglino, SSM Salehian, J Koutník
arXiv preprint arXiv:2002.10451, 2020
Principles of LASSO MPC
M Gallieri, M Gallieri
Lasso-MPC–Predictive Control with ℓ1-Regularised Least Squares, 47-63, 2016
Model predictive control with prioritised actuators
M Gallieri, JM Maciejowski
2015 European Control Conference (ECC), 533-538, 2015
ℓasso-MPC-predictive control with ℓ₁-regularised least squares
M Gallieri
University of Cambridge, 2014
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