World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
52
Citations
10655
World Ranking
2521
National Ranking
140

David H. Owens publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where David H. Owens sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 534 publications — 87th percentile

87% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

David H. Owens D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where David H. Owens sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 52 D-Index — 64th percentile

64% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2008 - Fellow of the Royal Academy of Engineering (UK)

Overview

What is he best known for?

The fields of study he is best known for:

  • Control theory
  • Artificial intelligence
  • Mathematical analysis

His main research concerns Iterative learning control, Control theory, Mathematical optimization, Linear system and Stability. The study incorporates disciplines such as Optimal control, Iterative method, Rate of convergence, Monotonic function and Robustness in addition to Iterative learning control. The various areas that David H. Owens examines in his Optimal control study include Norm, Discrete time and continuous time and Observability.

His Control theory research includes elements of Feature and Applied mathematics. His Mathematical optimization study integrates concerns from other disciplines, such as Weighting, Intelligent control and Newton's method. His Linear system research is multidisciplinary, relying on both Basis and Of the form.

His most cited work include:

  • Iterative learning control for discrete-time systems with exponential rate of convergence (313 citations)
  • Stability Analysis for Linear Repetitive Processes (260 citations)
  • Iterative learning control using optimal feedback and feedforward actions (256 citations)

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

David H. Owens spends much of his time researching Control theory, Iterative learning control, Mathematical optimization, Linear system and Control engineering. All of his Control theory and Multivariable calculus, Adaptive control, Robustness, Repetitive control and Control system investigations are sub-components of the entire Control theory study. As a member of one scientific family, David H. Owens mostly works in the field of Iterative learning control, focusing on Optimal control and, on occasion, Discrete time and continuous time.

David H. Owens combines subjects such as Model predictive control and Nonlinear system with his study of Mathematical optimization. Basis is closely connected to Stability in his research, which is encompassed under the umbrella topic of Linear system. His study in the fields of Control theory under the domain of Control engineering overlaps with other disciplines such as Process control.

He most often published in these fields:

  • Control theory (64.36%)
  • Iterative learning control (36.97%)
  • Mathematical optimization (24.73%)

What were the highlights of his more recent work (between 2008-2020)?

  • Iterative learning control (36.97%)
  • Mathematical optimization (24.73%)
  • Norm (11.97%)

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

His primary areas of study are Iterative learning control, Mathematical optimization, Norm, Control theory and Monotonic function. His Iterative learning control research integrates issues from Algorithm, Algorithm design, Optimal control and Robustness. His biological study spans a wide range of topics, including Weighting, Linear system and Fixed-point iteration.

His Norm study combines topics from a wide range of disciplines, such as Embedding and Multivariable calculus. His Control theory study frequently draws connections between adjacent fields such as Motion control. The Monotonic function study combines topics in areas such as Dykstra's projection algorithm, Hilbert space, Tracking error, Applied mathematics and Rate of convergence.

Between 2008 and 2020, his most popular works were:

  • Iterative Learning Control: An Optimization Paradigm (181 citations)
  • Robust monotone gradient-based discrete-time iterative learning control (80 citations)
  • Norm-Optimal Iterative Learning Control With Intermediate Point Weighting: Theory, Algorithms, and Experimental Evaluation (56 citations)

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

  • Control theory
  • Artificial intelligence
  • Mathematical analysis

Iterative learning control, Mathematical optimization, Repetitive control, Norm and Optimal control are his primary areas of study. Control theory and Artificial intelligence are the subject areas of his Iterative learning control study. His primary area of study in Control theory is in the field of Robustness.

David H. Owens studied Mathematical optimization and Weighting that intersect with Linear system, Projection and Iterative method. As part of the same scientific family, David H. Owens usually focuses on Repetitive control, concentrating on Intelligent control and intersecting with Projection method, Remainder, Rehabilitation robotics and Robotics. His biological study spans a wide range of topics, including Algorithm, Algebraic number and Multivariable calculus.

Best Publications

  • Computer-Aided Control System Design

    H. H. Rosenbrock;D. H. Owens

  • Control Systems Theory and Applications for Linear Repetitive Processes

    Eric Rogers;Krzysztof Galkowski;D. H. Owens

  • Iterative learning control for discrete-time systems with exponential rate of convergence

    N. Amann;D.H. Owens;E. Rogers

  • Stability Analysis for Linear Repetitive Processes

    E. T. A. Rogers;D. H. Owens

  • Iterative learning control using optimal feedback and feedforward actions

    Notker Amann;David H. Owens;Eric Rogers

  • Predictive optimal iterative learning control

    Notker Amann;David H. Owens;Eric Rogers

  • Iterative Learning Control: An Optimization Paradigm

    David H. Owens

  • Analysis of Linear Iterative Learning Control Schemes -A 2D Systems/Repetitive Processes Approach

    D. H. Owens;N. Amann;E. Rogers;M. French

  • Parameter optimization in iterative learning control

    D. H. Owens;K. Feng

  • LMIs - a fundamental tool in analysis and controller design for discrete linear repetitive processes

    K. Galkowski;E. Rogers;S. Xu;J. Lam

  • Norm-Optimal Iterative Learning Control Applied to Gantry Robots for Automation Applications

    J.D. Ratcliffe;P.L. Lewin;E. Rogers;J.J. Hatonen

  • Robust monotone gradient-based discrete-time iterative learning control

    D. H. Owens;J. J. Hatonen;S. Daley

  • Sufficient conditions for stability of linear time-varying systems

    A. Ilchmann;D. H. Owens;D. Prätzel-Wolters

  • Existence and learning of oscillations in recurrent neural networks

    S. Townley;A. Ilchmann;M.G. Weiss;W. Mcclements

  • Method and apparatus for improved application program switching on a computer-controlled display system

    David H. Owens;Stephen Fisher

  • An algebraic approach to iterative learning control

    J. J. Hätönen;D. H. Owens;K. L. Moore

  • Discrete-time inverse model-based iterative learning control: stability, monotonicity and robustness

    T. J. Harte;J. Hätönen;D. H. Owens

  • Consistency and Liapunov Stability of Linear Descriptor Systems: A Geometric Analysis

    David H. Owens;Dragutin Lj. Debeljkovic

  • Feedback and multivariable systems

    D. H. Owens

  • Stability and control of differential linear repetitive processes using an LMI setting.

    Krzysztof Galkowski;Wojciech Paszke;Eric Rogers;Shengyuan Xu

Frequent Co-Authors

Eric Rogers
Eric Rogers University of Southampton
Krzysztof Galkowski
Krzysztof Galkowski University of Zielona Góra
Paul Lewin
Paul Lewin University of Southampton
Chris Freeman
Chris Freeman Bangor University
Yi Cao
Yi Cao Lund University
Stephen A. Billings
Stephen A. Billings University of Sheffield
James Lam
James Lam University of Hong Kong
Shengyuan Xu
Shengyuan Xu Nanjing University of Science and Technology
Kevin L. Moore
Kevin L. Moore Colorado School of Mines
Michael J. Grimble
Michael J. Grimble University of Strathclyde

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