Daniel G. Sbarbaro-Hofer
Daniel G. Sbarbaro-Hofer
Verified email at
Cited by
Cited by
Neural networks for control systems—a survey
KJ Hunt, D Sbarbaro, R ¯bikowski, PJ Gawthrop
Automatica 28 (6), 1083-1112, 1992
Neural networks for nonlinear internal model control
KJ Hunt, D Sbarbaro
IEE Proceedings D (Control Theory and Applications) 138 (5), 431-438, 1991
Multiobjective switching state selector for finite-states model predictive control based on fuzzy decision making in a matrix converter
F Villarroel, JR Espinoza, CA Rojas, J Rodriguez, M Rivera, D Sbarbaro
IEEE Transactions on Industrial Electronics 60 (2), 589-599, 2012
Design of a discrete-time linear control strategy for a multicell UPQC
JA Muñoz, JR Espinoza, CR Baier, LA Morán, EE Espinosa, PE Melin, ...
IEEE Transactions on industrial electronics 59 (10), 3797-3807, 2011
An adaptive sliding-mode controller for discrete nonlinear systems
D Munoz, D Sbarbaro
IEEE transactions on industrial electronics 47 (3), 574-581, 2000
Advanced control and supervision of mineral processing plants
D Sbárbaro, R Del Villar
Springer Science & Business Media, 2010
Irreversible port-Hamiltonian systems: A general formulation of irreversible processes with application to the CSTR
H Ramirez, B Maschke, D Sbarbaro
Chemical Engineering Science 89, 223-234, 2013
Neural control of a steel rolling mill
D Sbarbaro-Hofer, D Neumerkel, K Hunt
IEEE Control Systems Magazine 13 (3), 69-75, 1993
On the control of non-linear processes: An IDA–PBC approach
H Ramirez, D Sbarbaro, R Ortega
Journal of Process Control 19 (3), 405-414, 2009
Nonlinear adaptive control using nonparametric Gaussian process prior models
R Murray-Smith, D Sbarbaro
IFAC Proceedings Volumes 35 (1), 325-330, 2002
Adaptive, cautious, predictive control with Gaussian process priors
R Murray-Smith, D Sbarbaro, CE Rasmussen, A Girard
IFAC Proceedings Volumes 36 (16), 1155-1160, 2003
R. bikowski, and P. Gawthrop
K Hunt, D Sbarbaro
Neural networks for control systems-a survey. Automatica 28 (6), 1083-1112, 1992
Observer-based event-triggered control co-design for linear systems
S Tarbouriech, A Seuret, JMG da Silva, D Sbarbaro
IET Control Theory & Applications 10 (18), 2466-2473, 2016
Neural networks for modelling and control of a non-linear dynamic system
R Murray-Smith, D Neumerkel, D Sbarbaro-Hofer
Institute of Electrical and Electronics Engineers (IEEE), 1992
On the spectral bands measurements for combustion monitoring
L Arias, S Torres, D Sbarbaro, P Ngendakumana
Combustion and Flame 158 (3), 423-433, 2011
Adaptive soft-sensors for on-line particle size estimation in wet grinding circuits
D Sbarbaro, P Ascencio, P Espinoza, F Mujica, G Cortes
Control Engineering Practice 16 (2), 171-178, 2008
Optimal control of a rougher flotation process based on dynamic programming
M Maldonado, D Sbarbaro, E Lizama
Minerals engineering 20 (3), 221-232, 2007
Real-time monitoring and characterization of flames by principal-component analysis
D Sbarbaro, O Farias, A Zawadsky
Combustion and flame 132 (3), 591-595, 2003
Repetitive control design for MIMO systems with saturating actuators
JV Flores, JMG da Silva, LFA Pereira, DG Sbarbaro
IEEE Transactions on Automatic Control 57 (1), 192-198, 2011
Outliers detection in environmental monitoring databases
H Garces, D Sbarbaro
Engineering Applications of Artificial Intelligence 24 (2), 341-349, 2011
The system can't perform the operation now. Try again later.
Articles 1–20