His primary areas of study are Model predictive control, Control engineering, Control theory, Control theory and Control. His Model predictive control research is multidisciplinary, relying on both Cruise control, Simulation and Optimal control. His research in Simulation focuses on subjects like Intelligent agent, which are connected to Reinforcement learning.
His Control engineering study combines topics from a wide range of disciplines, such as Particle swarm optimization, Distributed computing, Automatic control and Benchmark. His research in Control theory intersects with topics in Road traffic control and Nonlinear system. His Control research incorporates elements of Computation and Mathematical optimization.
B. De Schutter focuses on Model predictive control, Control theory, Mathematical optimization, Control engineering and Control. His research integrates issues of Optimal control, Control theory, Simulation, Benchmark and Optimization problem in his study of Model predictive control. In Control theory, B. De Schutter works on issues like Fuzzy logic, which are connected to Stability.
His Mathematical optimization research incorporates themes from Control system and Computation. His research in the fields of Adaptive control overlaps with other disciplines such as Electric power system. His work carried out in the field of Control brings together such families of science as Multi-agent system and Transport engineering.
B. De Schutter mainly focuses on Mathematical optimization, Model predictive control, Control theory, State and Control. His Mathematical optimization research is multidisciplinary, incorporating elements of Traffic flow, Job shop scheduling and Benchmark. His Benchmark research includes elements of Nonlinear model, 2-opt, Distributed model predictive control and Multi-agent system.
B. De Schutter focuses mostly in the field of Model predictive control, narrowing it down to matters related to Automotive engineering and, in some cases, Optimization problem. His studies in Control theory integrate themes in fields like Control engineering, Human-in-the-loop and Curse of dimensionality. His study in Control theory is interdisciplinary in nature, drawing from both State vector and Reinforcement learning.
The scientist’s investigation covers issues in Real-time computing, Reliability engineering, Control theory, Control theory and Model predictive control. Real-time computing is closely attributed to Bayesian network in his work. His work on Proactive maintenance, Planned maintenance and Condition-based maintenance as part of general Reliability engineering study is frequently connected to Corrective maintenance and Spare part, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His research on Control theory frequently links to adjacent areas such as Automotive engineering. The Control theory study combines topics in areas such as Curse of dimensionality and Reinforcement learning. His Model predictive control study is associated with Control.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A Comprehensive Survey of Multiagent Reinforcement Learning
L. Busoniu;R. Babuska;B. De Schutter.
systems man and cybernetics (2008)
Brief Equivalence of hybrid dynamical models
W. P. M. H. Heemels;B. De Schutter;A. Bemporad.
Multi-agent model predictive control for transportation networks: Serial versus parallel schemes
R. R. Negenborn;B. De Schutter;J. Hellendoorn.
Engineering Applications of Artificial Intelligence (2008)
Model predictive control for ramp metering of motorway traffic: A case study
T. Bellemans;B. De Schutter;B. De Moor.
Control Engineering Practice (2006)
Minimal state-space realization in linear system theory: an overview
B. De Schutter.
Journal of Computational and Applied Mathematics (2000)
Fast Model Predictive Control for Urban Road Networks via MILP
Shu Lin;B. De Schutter;Yugeng Xi;H. Hellendoorn.
IEEE Transactions on Intelligent Transportation Systems (2011)
A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark
I. Alvarado;D. Limon;D. Muñoz de la Peña;J.M. Maestre.
Journal of Process Control (2011)
Optimal traffic light control for a single intersection
B. De Schutter.
american control conference (1999)
Adaptive Cruise Control for a SMART Car: A Comparison Benchmark for MPC-PWA Control Methods
D. Corona;B. De Schutter.
IEEE Transactions on Control Systems and Technology (2008)
Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes
S.K. Zegeye;B. De Schutter;J. Hellendoorn;E.A. Breunesse.
Transportation Research Part C-emerging Technologies (2013)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: