Bart De Schutter
Bart De Schutter
full professor & head of department, Delft Center for Systems and Control
Verified email at - Homepage
Cited by
Cited by
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
Reinforcement learning and dynamic programming using function approximators
L Busoniu, R Babuska, B De Schutter, D Ernst
CRC press, 2017
Equivalence of hybrid dynamical models
WPMH Heemels, B De Schutter, A Bemporad
Automatica 37 (7), 1085-1091, 2001
Multi-agent reinforcement learning: An overview
L Buşoniu, R Babuška, B De Schutter
Innovations in multi-agent systems and applications-1, 183-221, 2010
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
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
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
Stability analysis and nonlinear observer design using Takagi-Sugeno fuzzy models
Z Lendek, TM Guerra, R Babuska, B De Schutter
Springer Berlin Heidelberg, 2011
Model predictive control for max-plus-linear discrete event systems
B De Schutter, T Van Den Boom
Automatica 37 (7), 1049-1056, 2001
DAISY: A database for identification of systems
B De Moor, P De Gersem, B De Schutter, W Favoreel
JOURNAL A 38 (4), 5, 1997
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
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
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
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark
J Lago, G Marcjasz, B De Schutter, R Weron
Applied Energy 293, 116983, 2021
Demand response with micro-CHP systems
M Houwing, RR Negenborn, B De Schutter
Proceedings of the IEEE 99 (1), 200-213, 2011
Optimal traffic light control for a single intersection
B De Schutter, B De Moor
European Journal of Control 4 (3), 260-276, 1998
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
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
Distributed model predictive control of irrigation canals
RR Negenborn, PJ van Overloop, T Keviczky, B De Schutter
Networks and heterogeneous media 4 (2), 359-380, 2009
A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark
I Alvarado, D Limon, DM De La Peña, JM Maestre, MA Ridao, H Scheu, ...
Journal of Process Control 21 (5), 800-815, 2011
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