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Matteo Rubagotti
Matteo Rubagotti
Professor of Robotics, Nazarbayev University
Dirección de correo verificada de nu.edu.kz - Página principal
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Integral sliding mode control for nonlinear systems with matched and unmatched perturbations
M Rubagotti, A Estrada, F Castanos, A Ferrara, L Fridman
IEEE Transactions on Automatic Control 56 (11), 2699-2704, 2011
3342011
Sliding mode control of constrained nonlinear systems
GP Incremona, M Rubagotti, A Ferrara
IEEE Transactions on Automatic Control 62 (6), 2965–2972, 2017
2302017
Robust model predictive control with integral sliding mode in continuous-time sampled-data nonlinear systems
M Rubagotti, DM Raimondo, A Ferrara, L Magni
IEEE Transactions on Automatic Control 56 (3), 556-570, 2011
2142011
A sub-optimal second order sliding mode controller for systems with saturating actuators
A Ferrara, M Rubagotti
IEEE Transactions on Automatic Control 54 (5), 1082-1087, 2009
1222009
Perceived safety in physical human robot interaction - A survey
M Rubagotti, I Tusseyeva, S Baltabayeva, D Summers, A Sandygulova
Robotics and Autonomous Systems 151, 104047, 2022
1122022
Haptic devices: wearability-based taxonomy and literature review
A Adilkhanov, M Rubagotti, Z Kappassov
IEEE Access 10, 91923-91947, 2022
772022
Semi-autonomous robot teleoperation with obstacle avoidance via model predictive control
M Rubagotti, T Taunyazov, B Omarali, A Shintemirov
IEEE Robotics and Automation Letters 4 (3), 2746-2753 (presented at RSS 2019), 2019
732019
Optimal energy management of a small-size building via hybrid model predictive control
A Khakimova, A Kusatayeva, A Shamshimova, D Sharipova, A Bemporad, ...
Energy and Buildings 140, 1-8, 2017
722017
Stabilizing linear model predictive control under inexact numerical optimization
M Rubagotti, P Patrinos, A Bemporad
IEEE Transactions on Automatic Control 59 (5), 1660-1666, 2014
712014
Adaptive super-twisting sliding mode control for maximum power point tracking of PMSG-based wind energy conversion systems
D Zholtayev, M Rubagotti, TD Do
Renewable Energy 183, 877-889, 2022
632022
Second-order sliding-mode control of a mobile robot based on a harmonic potential field
A Ferrara, M Rubagotti
IET Control Theory & Applications 2 (9), 807-818, 2008
632008
Closed-loop control of variable stiffness actuated robots via nonlinear model predictive control
A Zhakatayev, M Rubagotti, HA Varol
IEEE Access 3, 235-248, 2015
552015
Time-optimal sliding-mode control of a mobile robot in a dynamic environment
M Rubagotti, ML Della Vedova, A Ferrara
IET Control Theory & Applications 5 (16), 1916-1924, 2011
482011
A general framework for switched and variable-gain higher-order sliding mode control
GP Incremona, M Rubagotti, M Tanelli, A Ferrara
IEEE Transactions on Automatic Control 66 (4), 1718-1724, 2021
442021
Sliding mode control of a mobile robot for dynamic obstacle avoidance based on a time-varying harmonic potential field
A Ferrara, M Rubagotti
ICRA Workshop: Planning, Perception and Navigation for Intelligent Vehicles, 2007
442007
Second order sliding mode control of a perturbed double integrator with state constraints
M Rubagotti, A Ferrara
American Control Conference, 2010
432010
Diagnosis and prognosis of automotive systems: motivations, history and some results
G Rizzoni, S Onori, M Rubagotti
IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2009
382009
Numerical optimal control of a spherical parallel manipulator based on unique kinematic solutions
A Shintemirov, A Niyetkaliyev, M Rubagotti
IEEE/ASME Transactions on Mechatronics 21 (1), 98-109, 2016
362016
Multirate sliding mode disturbance compensation for model predictive control
DM Raimondo, M Rubagotti, CN Jones, L Magni, A Ferrara, M Morari
International Journal of Robust and Nonlinear Control 25 (16), 2984–3003, 2015
342015
Constrained nonlinear discrete-time sliding mode control based on a receding horizon approach
M Rubagotti, GP Incremona, DM Raimondo, A Ferrara
IEEE Transactions on Automatic Control 66 (8), 3802-3809, 2021
332021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
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