H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 53 Citations 11,002 333 World Ranking 1236 National Ranking 28

Research.com Recognitions

Awards & Achievements

2019 - IEEE Fellow For contributions to optimal control of discrete-event and hybrid systems

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Control theory
  • Mathematical optimization

His primary areas of investigation include Model predictive control, Mathematical optimization, Control theory, Optimal control and Nonlinear system. His research in Model predictive control intersects with topics in Control engineering, Cogeneration, Speed limit and Traffic flow. His work in Mathematical optimization addresses subjects such as Nonlinear programming, which are connected to disciplines such as Integer programming.

His work on Control theory, Lyapunov function, Linear system and State variable as part of general Control theory study is frequently connected to Scale, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Optimal control research is multidisciplinary, incorporating perspectives in Routing, Aggregate, Grid network and Road traffic control. Bart De Schutter interconnects Operations research, Numerical linear algebra and Sequential quadratic programming in the investigation of issues within Nonlinear system.

His most cited work include:

  • Reinforcement Learning and Dynamic Programming Using Function Approximators (658 citations)
  • Model predictive control for optimal coordination of ramp metering and variable speed limits (442 citations)
  • Optimal coordination of variable speed limits to suppress shock waves (297 citations)

What are the main themes of his work throughout his whole career to date?

Bart De Schutter focuses on Mathematical optimization, Model predictive control, Control theory, Control and Optimization problem. His study in Mathematical optimization is interdisciplinary in nature, drawing from both Function, Computation and Computational complexity theory. The concepts of his Model predictive control study are interwoven with issues in Traffic flow, Reduction, Control engineering, Control theory and Simulation.

His Control theory study frequently links to adjacent areas such as Bounded function. His Control research includes themes of Routing and Transport engineering. His Linear system study frequently links to related topics such as Algorithm.

He most often published in these fields:

  • Mathematical optimization (37.33%)
  • Model predictive control (32.26%)
  • Control theory (27.88%)

What were the highlights of his more recent work (between 2017-2021)?

  • Mathematical optimization (37.33%)
  • Control theory (27.88%)
  • Model predictive control (32.26%)

In recent papers he was focusing on the following fields of study:

His main research concerns Mathematical optimization, Control theory, Model predictive control, Optimization problem and Control. His Mathematical optimization research integrates issues from Distributed generation, State and Benchmark. He combines subjects such as Bottleneck and Speed limit with his study of Control theory.

His work carried out in the field of Model predictive control brings together such families of science as Probabilistic logic, Microgrid and Optimal control. His work deals with themes such as Function and Distributed algorithm, which intersect with Optimization problem. His Control study combines topics in areas such as Control system and Reduction.

Between 2017 and 2021, his most popular works were:

  • Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms (187 citations)
  • Forecasting day-ahead electricity prices in Europe : The importance of considering market integration (89 citations)
  • A novel Lyapunov function for a non-weighted L2 gain of asynchronously switched linear systems (61 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer network
  • Control theory

Bart De Schutter spends much of his time researching Model predictive control, Control theory, Mathematical optimization, Optimization problem and Linear programming. Model predictive control is a subfield of Control that he explores. His Control theory research is multidisciplinary, relying on both Bottleneck, Speed limit, Control algorithm and Bounded function.

His research integrates issues of Phasor, Control theory, Probabilistic logic and State in his study of Mathematical optimization. He studied Linear programming and Nonlinear programming that intersect with Regenerative brake. The concepts of his Linear system study are interwoven with issues in Concurrency and Algebra.

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.

Top Publications

Reinforcement Learning and Dynamic Programming Using Function Approximators

Lucian Busoniu;Robert Babuska;Bart De Schutter;Damien Ernst.
(2010)

991 Citations

Model predictive control for optimal coordination of ramp metering and variable speed limits

Andreas Hegyi;Bart De Schutter;Hans Hellendoorn.
Transportation Research Part C-emerging Technologies (2005)

671 Citations

Optimal coordination of variable speed limits to suppress shock waves

A. Hegyi;Bart De Schutter;J. Hellendoorn.
IEEE Transactions on Intelligent Transportation Systems (2005)

428 Citations

Brief Model predictive control for max-plus-linear discrete event systems

Bart De Schutter;Ton Van Den Boom.
Automatica (2001)

398 Citations

Multi-agent Reinforcement Learning: An Overview

Lucian Buşoniu;Robert Babuška;Bart De Schutter.
(2010)

367 Citations

DAISY : A database for identification of systems

Bart De Moor;P De Gersem;Bart De Schutter;W Favoreel.
Journal A (1997)

326 Citations

Stability Analysis and Nonlinear Observer Design using Takagi-Sugeno Fuzzy Models

Robert Babuska;Bart De Schutter;Zsfia Lendek;T. M. Guerra.
(2010)

325 Citations

Demand Response With Micro-CHP Systems

Michiel Houwing;Rudy R Negenborn;Bart De Schutter.
Proceedings of the IEEE (2011)

276 Citations

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

Jesus Lago;Fjo De Ridder;Bart De Schutter.
Applied Energy (2018)

270 Citations

Distributed model predictive control of irrigation canals

Rudy R. Negenborn;Peter-Jules van Overloop;Tamás Keviczky;Bart De Schutter.
Networks and Heterogeneous Media (2009)

248 Citations

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.

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Top Scientists Citing Bart De Schutter

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