Bart De Schutter

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Hans HellendoornHoogleraar regeltechniek, TU DelftVerified email at tudelft.nl
Robert BabuskaProfessor, Delft University of Technology, Czech Technical University PragueVerified email at tudelft.nl
Ton van den boomAssociate professor, Delft University of TechnologyVerified email at tudelft.nl
Rudy R. NegenbornFull professor, Delft University of TechnologyVerified email at tudelft.nl
Andreas HegyiDelft University of TechnologyVerified email at tudelft.nl
Lucian BusoniuProfessor and Group Lead, Automation Department, Technical University of Cluj-NapocaVerified email at aut.utcluj.ro
De Moor BartKU Leuven ESAT-StadiusVerified email at esat.kuleuven.be
Alfredo NúñezAssociate Professor on Intelligent Railway Infrastructure, TUDelftVerified email at tudelft.nl
Simone BaldiProfessor at Southeast UniversityVerified email at seu.edu.cn
Yihui WangBeijing Jiaotong UniversityVerified email at bjtu.edu.cn
Zsofia LendekTechnical University of Cluj-NapocaVerified email at aut.utcluj.ro
Maurice HeemelsFull Professor Eindhoven University of TechnologyVerified email at tue.nl
Tamas KeviczkyDelft Center for Sytems and Control, Delft University of TechnologyVerified email at tudelft.nl
Shu LinAssociate Professor of Computer and Contol Engineering, University of Chinese Academy of SciencesVerified email at ucas.ac.cn
Michel VerhaegenProfessor Systems and Control, Delft University of TechnologyVerified email at tudelft.nl
Jesus LagoAmazonVerified email at amazon.com
Damien ErnstProfessor of Electrical Engineering and Computer Science, ULiègeVerified email at uliege.be
L. D. BaskarDelft university of technologyVerified email at sharethis.com
Minh Dang DoanUniversity of FreiburgVerified email at doan.vn
Alberto BemporadProfessor of Control Systems, IMT Lucca, ItalyVerified email at imtlucca.it

Bart De Schutter

full professor & head of department, Delft Center for Systems and Control

Verified email at tudelft.nl - Homepage

control of large-scale systems multi-agent multi-level control hybrid systems machine learning


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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	2811	2008
Reinforcement learning and dynamic programming using function approximators L Busoniu, R Babuska, B De Schutter, D Ernst CRC press, 2017	1271	2017
Multi-agent reinforcement learning: An overview L Buşoniu, R Babuška, B De Schutter Innovations in multi-agent systems and applications-1, 183-221, 2010	1004	2010
Equivalence of hybrid dynamical models WPMH Heemels, B De Schutter, A Bemporad Automatica 37 (7), 1085-1091, 2001	973	2001
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	789	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	662	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	509	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	453	2011
Model predictive control for max-plus-linear discrete event systems B De Schutter, T Van Den Boom Automatica 37 (7), 1049-1056, 2001	424	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	413	1997
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	412	2016
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	404	2006
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	365	2021
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	357	2016
Demand response with micro-CHP systems M Houwing, RR Negenborn, B De Schutter Proceedings of the IEEE 99 (1), 200-213, 2011	315	2011
Optimal traffic light control for a single intersection B De Schutter, B De Moor European Journal of Control 4 (3), 260-276, 1998	300	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	294	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	294	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	286	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	261	2011

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