World's Best Scientists 2026 revealed!
J. De Schutter

J. De Schutter

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Mechanical and Aerospace Engineering
Belgium
2026

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
55
Citations
12201
World Ranking
895
National Ranking
8

Electronics and Electrical Engineering

D-Index
53
Citations
12035
World Ranking
2380
National Ranking
52

J. De Schutter publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where J. De Schutter sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 361 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

J. De Schutter D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where J. De Schutter sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 55 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Mechanical and Aerospace Engineering in Belgium Leader Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Control theory
  • Geometry

J. De Schutter spends much of his time researching Control theory, Robot, Control engineering, Motion control and Industrial robot. His Robot research is under the purview of Artificial intelligence. His study in Control engineering is interdisciplinary in nature, drawing from both Robot motion, Structure, Simulation, Robot control and Resolution.

His work in Motion control tackles topics such as Adaptive control which are related to areas like Motion estimation and Social robot. His research integrates issues of Mathematical optimization, Optimal control, Motion planning and Convex optimization in his study of Robot kinematics. The concepts of his Robotics study are interwoven with issues in Tracking error, Robot end effector and Feed forward.

His most cited work include:

  • KRAS wild-type state predicts survival and is associated to early radiological response in metastatic colorectal cancer treated with cetuximab (736 citations)
  • Optimal robot excitation and identification (373 citations)
  • Time-Optimal Path Tracking for Robots: A Convex Optimization Approach (333 citations)

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

His primary areas of investigation include Control theory, Robot, Control engineering, Artificial intelligence and Motion control. His work investigates the relationship between Control theory and topics such as Mathematical optimization that intersect with problems in Convex optimization and Spline. His work deals with themes such as Motion and Simulation, which intersect with Robot.

His Control engineering study which covers Task that intersects with Object. His Artificial intelligence research includes elements of Algorithm and Computer vision. His Motion control research is multidisciplinary, incorporating perspectives in Adaptive control and Robust control.

He most often published in these fields:

  • Control theory (45.98%)
  • Robot (36.21%)
  • Control engineering (26.44%)

What were the highlights of his more recent work (between 2008-2016)?

  • Control theory (45.98%)
  • Mathematical optimization (9.20%)
  • Torque (9.20%)

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

The scientist’s investigation covers issues in Control theory, Mathematical optimization, Torque, Convex optimization and Gait. His work carried out in the field of Control theory brings together such families of science as Simple and Simulation. His Simulation study incorporates themes from Robotics, System identification, Inverse dynamics and Artificial intelligence.

His Control theory study introduces a deeper knowledge of Control engineering. His study explores the link between Control engineering and topics such as Automation that cross with problems in Video tracking. His biological study spans a wide range of topics, including Robot, Robot kinematics and Mobile robot.

Between 2008 and 2016, his most popular works were:

  • Time-Optimal Path Tracking for Robots: A Convex Optimization Approach (333 citations)
  • Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors (66 citations)
  • A physiology based inverse dynamic analysis of human gait: potential and perspectives. (39 citations)

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

  • Artificial intelligence
  • Control theory
  • Geometry

His scientific interests lie mostly in Control theory, Sensitivity, Motion control, Optimal control and Biomechanics. His research is interdisciplinary, bridging the disciplines of Simulation and Control theory. His Sensitivity study combines topics from a wide range of disciplines, such as Control theory, Adaptive control, Repetitive control and Setpoint.

His study on Motion control is covered under Robot. His Optimal control research is multidisciplinary, relying on both Motion planning and Robustness. The Biomechanics study which covers Gait that intersects with Isometric exercise, Torque and Physical therapy.

Best Publications

  • Optimal robot excitation and identification

    J. Swevers;C. Ganseman;D.B. Tukel;J. de Schutter

  • Time-Optimal Path Tracking for Robots: A Convex Optimization Approach

    D. Verscheure;B. Demeulenaere;J. Swevers;J. De Schutter

  • Compliant robot motion: I. A formalism for specifying compliant motion tasks

    J. De Schutter;H. van Brussel

  • Dynamic Model Identification for Industrial Robots

    J. Swevers;W. Verdonck;J. De Schutter

  • Kalman filters for non-linear systems: a comparison of performance

    Tine Lefebvre;Herman Bruyninckx;Joris De Schutter

  • Compliant robot motion II. A control approach based on external control loops

    J. De Schutter;H. van Brussel

  • Specification of force-controlled actions in the "task frame formalism"-a synthesis

    H. Bruyninckx;J. De Schutter

  • Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty

    Joris De Schutter;Tinne De Laet;Johan Rutgeerts;Wilm Decré

  • Force Control

    Unknown

  • A smoothly constrained Kalman filter

    J. De Geeter;H. Van Brussel;J. De Schutter;M. Decreton

  • Human-inspired robot assistant for fast point-to-point movements

    B. Corteville;E. Aertbelien;H. Bruyninckx;J. De Schutter

  • Brief paper: Robust high-order repetitive control: Optimal performance trade-offs

    Goele Pipeleers;Bram Demeulenaere;Joris De Schutter;Jan Swevers

  • Extended Bandwidth Zero Phase Error Tracking Control of Nonminimal Phase Systems

    D. Torfs;J. De Schutter;J. Swevers

  • Force control: A bird's eye view

    Joris De Schutter;Herman Bruyninckx;Wen-Hong Zhu;Mark W. Spong

  • Kalman smoothing improves the estimation of joint kinematics and kinetics in marker-based human gait analysis.

    F. De Groote;T. De Laet;I. Jonkers;J. De Schutter

  • Force Control

    Unknown

  • EXPERIMENTAL ROBOT IDENTIFICATION USING OPTIMISED PERIODIC TRAJECTORIES

    J. Swevers;C. Ganseman;J. De Schutter;H. Van Brussel

  • Servo motor selection criterion for mechatronic applications

    H.J. van de Straete;P. Degezelle;J. De Schutter;R.J.M. Belmans

  • eTaSL/eTC: A constraint-based task specification language and robot controller using expression graphs.

    Erwin Aertbeliën;Joris De Schutter

  • Virtual decomposition based control for generalized high dimensional robotic systems with complicated structure

    Wen-Hong Zhu;Yu-Geng Xi;Zhong-Jun Zhang;Zeungnam Bien

  • Sensitivity of dynamic simulations of gait and dynamometer experiments to hill muscle model parameters of knee flexors and extensors

    F. De Groote;A. Van Campen;I. Jonkers;J. De Schutter

  • Peg-on-hole: a model based solution to peg and hole alignment

    H. Bruyninckx;S. Dutre;J. De Schutter

  • A self-learning automaton with variable resolution for high precision assembly by industrial robots

    J. Simons;H. Brussel;J. de Schutter;J. Verhaert

  • Time-energy optimal path tracking for robots: a numerically efficient optimization approach

    D. Verscheure;B. Demeulenaere;J. Swevers;J. De Schutter

  • Influence of propeller configuration on propulsion system efficiency of multi-rotor Unmanned Aerial Vehicles

    B. Theys;G. Dimitriadis;P. Hendrick;J. De Schutter

  • Hybrid vision/force control at corners in planar robotic-contour following

    J. Baeten;J. De Schutter

  • Compliant Robot motion. I: A formalism for specifying compliant motion tasks. II. A control approach based on external control loops

    J. De Schutter;H. Van Brussel

Frequent Co-Authors

Jan Swevers
Jan Swevers KU Leuven
Ilse Jonkers
Ilse Jonkers KU Leuven
Sabine Tejpar
Sabine Tejpar KU Leuven
Marc Peeters
Marc Peeters University of Antwerp
Bart Jacobs
Bart Jacobs Radboud University
Moritz Diehl
Moritz Diehl University of Freiburg
Lyudmila Mihaylova
Lyudmila Mihaylova University of Sheffield

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