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Bart De Schutter
Bart De Schutter
full professor. hybrid control and intelligent transportation systems, Delft University of
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A comprehensive survey of multiagent reinforcement learning
L Busoniu, R Babuska, B De Schutter
IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and …, 2008
18332008
Reinforcement learning and dynamic programming using function approximators
L Busoniu, R Babuska, B De Schutter, D Ernst
CRC press, 2017
10672017
Equivalence of hybrid dynamical models
WPMH Heemels, B De Schutter, A Bemporad
Automatica 37 (7), 1085-1091, 2001
9012001
Model predictive control for optimal coordination of ramp metering and variable speed limits
A Hegyi, B De Schutter, H Hellendoorn
Transportation Research Part C: Emerging Technologies 13 (3), 185-209, 2005
7062005
Multi-agent reinforcement learning: An overview
L Buşoniu, R Babuška, BD Schutter
Innovations in multi-agent systems and applications-1, 183-221, 2010
5452010
Optimal coordination of variable speed limits to suppress shock waves
A Hegyi, B De Schutter, J Hellendoorn
IEEE Transactions on intelligent transportation systems 6 (1), 102-112, 2005
4452005
Model predictive control for max-plus-linear discrete event systems
B De Schutter, T Van Den Boom
Automatica 37 (7), 1049-1056, 2001
4052001
Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
J Lago, F De Ridder, B De Schutter
Applied Energy 221, 386-405, 2018
3932018
Stability analysis and nonlinear observer design using Takagi-Sugeno fuzzy models
Z Lendek, TM Guerra, R Babuska, B De Schutter
Springer Berlin Heidelberg, 2011
3562011
DAISY: A database for identification of systems
B De Moor, P De Gersem, B De Schutter, W Favoreel
JOURNAL A 38, 4-5, 1997
3531997
Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations
O Gietelink, J Ploeg, B De Schutter, M Verhaegen
Vehicle System Dynamics 44 (7), 569-590, 2006
3342006
Multi-agent model predictive control for transportation networks: Serial versus parallel schemes
RR Negenborn, B De Schutter, J Hellendoorn
Engineering Applications of Artificial Intelligence 21 (3), 353-366, 2008
2922008
Demand response with micro-CHP systems
M Houwing, RR Negenborn, B De Schutter
Proceedings of the IEEE 99 (1), 200-213, 2011
2902011
Deep convolutional neural networks for detection of rail surface defects
S Faghih-Roohi, S Hajizadeh, A Núñez, R Babuska, B De Schutter
2016 International joint conference on neural networks (IJCNN), 2584-2589, 2016
2682016
Optimal traffic light control for a single intersection
B De Schutter, B De Moor
European Journal of Control 4 (3), 260-276, 1998
2641998
Distributed model predictive control of irrigation canals
RR Negenborn, PJ van Overloop, T Keviczky, B De Schutter
Networks & Heterogeneous Media 4 (2), 359, 2009
2582009
Residential demand response of thermostatically controlled loads using batch reinforcement learning
F Ruelens, BJ Claessens, S Vandael, B De Schutter, R Babuška, ...
IEEE Transactions on Smart Grid 8 (5), 2149-2159, 2016
2432016
Accelerated gradient methods and dual decomposition in distributed model predictive control
P Giselsson, MD Doan, T Keviczky, B De Schutter, A Rantzer
Automatica 49 (3), 829-833, 2013
2392013
Model predictive control for ramp metering of motorway traffic: A case study
T Bellemans, B De Schutter, B De Moor
Control Engineering Practice 14 (7), 757-767, 2006
2272006
Traffic control and intelligent vehicle highway systems: a survey
LD Baskar, B De Schutter, J Hellendoorn, Z Papp
IET Intelligent Transport Systems 5 (1), 38-52, 2011
2242011
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Artículos 1–20