What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Algorithm
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.
His most cited work include:
- A Comprehensive Survey of Multiagent Reinforcement Learning (1182 citations)
- Brief Equivalence of hybrid dynamical models (635 citations)
- Multi-agent model predictive control for transportation networks: Serial versus parallel schemes (209 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Model predictive control (47.92%)
- Control theory (34.37%)
- Mathematical optimization (28.65%)
What were the highlights of his more recent work (between 2014-2020)?
- Mathematical optimization (28.65%)
- Model predictive control (47.92%)
- Control theory (34.37%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2020, his most popular works were:
- Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice (67 citations)
- Timely condition-based maintenance planning for multi-component systems (37 citations)
- A multi-class model-based control scheme for reducing congestion and emissions in freeway networks by combining ramp metering and route guidance (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Control theory
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.
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