World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
49
Citations
14610
World Ranking
2884
National Ranking
93

Overview

What is he best known for?

The fields of study he is best known for:

  • Control theory
  • Artificial intelligence
  • Programming language

His primary scientific interests are in Control theory, Control engineering, Nonlinear system, Control theory and Model predictive control. Self-tuning, Adaptive control, Intermittent control, Inverted pendulum and Compensation are the subjects of his Control theory studies. Peter J. Gawthrop combines subjects such as Control system and Basis function with his study of Control engineering.

The Nonlinear system study which covers Linear system that intersects with Observer, Torque, State space and Multivariable calculus. As a member of one scientific family, Peter J. Gawthrop mostly works in the field of Control theory, focusing on Stability and, on occasion, Range and Automatic control. Peter J. Gawthrop interconnects Nonlinear control and Optimal control in the investigation of issues within Model predictive control.

His most cited work include:

  • Neural networks for control systems: a survey (1614 citations)
  • A nonlinear disturbance observer for robotic manipulators (980 citations)
  • Self-tuning controller (761 citations)

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

Peter J. Gawthrop mainly focuses on Control theory, Control engineering, Bond graph, Model predictive control and Control theory. His Control theory study is mostly concerned with Nonlinear system, Intermittent control, Control system, Adaptive control and Self-tuning. His Nonlinear system study combines topics from a wide range of disciplines, such as Linear system and Mathematical optimization.

The Adaptive control study combines topics in areas such as Weighting and Robust control. His Control engineering research is multidisciplinary, incorporating elements of Control, Process control and Identification. His Model predictive control research integrates issues from Full state feedback, Mechanical system, Quadratic programming and Optimal control.

He most often published in these fields:

  • Control theory (53.98%)
  • Control engineering (32.74%)
  • Bond graph (28.32%)

What were the highlights of his more recent work (between 2015-2021)?

  • Bond graph (28.32%)
  • Energy based (5.31%)
  • Biological system (4.87%)

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

His main research concerns Bond graph, Energy based, Biological system, Systems biology and Mathematical model. His research integrates issues of Statistical physics, Living systems and Transduction in his study of Bond graph. His work in Energy based covers topics such as Feedback control which are related to areas like Control system, Transfer function, Control engineering, Energy feedback and Class.

His study in Control system is interdisciplinary in nature, drawing from both Feedback loop and Linear control. Energy feedback is often connected to Control theory in his work. His work carried out in the field of Systems biology brings together such families of science as Structure, Modularity and Correctness.

Between 2015 and 2021, his most popular works were:

  • Modular bond-graph modelling and analysis of biomolecular systems. (27 citations)
  • Bond Graph Modeling of Chemiosmotic Biomolecular Energy Transduction (22 citations)
  • Bond Graph Modelling of Chemiosmotic Biomolecular Energy Transduction (14 citations)

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

  • Control theory
  • Artificial intelligence
  • Programming language

His primary areas of study are Bond graph, Energy based, Biological system, Systems biology and Electron transport chain. His studies in Bond graph integrate themes in fields like Range and Robustness. His research on Energy based frequently connects to adjacent areas such as Transduction.

His Biophysics research extends to the thematically linked field of Systems biology. Peter J. Gawthrop combines Electron transport chain and Chemiosmosis in his studies. His Mathematical model research incorporates elements of Scale, Conservation law, Charge conservation, Physical system and Statistical physics.

Best Publications

  • Neural networks for control systems: a survey

    K. J. Hunt;D. Sbarbaro;R. Żbikowski;P. J. Gawthrop

  • A nonlinear disturbance observer for robotic manipulators

    Wen-Hua Chen;D.J. Ballance;P.J. Gawthrop;J. O'Reilly

  • Self-tuning controller

    D.W. Clarke;P.J. Gawthrop

  • Self-tuning control

    D.W. Clarke;P.J. Gawthrop

  • Brief Optimal control of nonlinear systems: a predictive control approach

    Wen-Hua Chen;Donald J. Ballance;Peter J Gawthrop

  • Metamodelling: for bond graphs and dynamic systems

    Peter Gawthrop;Lorcan Smith

  • Bond-graph modeling

    P.J. Gawthrop;G.P. Bevan

  • Human control of an inverted pendulum: Is continuous control necessary? Is intermittent control effective? Is intermittent control physiological?

    Ian D. Loram;Henrik Gollee;Martin Lakie;Peter J. Gawthrop

  • Intermittent control: a computational theory of human control

    Peter Gawthrop;Ian Loram;Martin Lakie;Henrik Gollee

  • Continuous-time generalized predictive control (CGPC)

    H. Demircioğlu;P. J. Gawthrop

  • Self-tuning PID controllers: Algorithms and implementation

    P. Gawthrop

  • Nonlinear PID predictive controller

    Wen-Hua Chen;Donald J. Ballance;Peter J. Gawthrop;Jenny J. Gribble

  • Some interpretations of the self-tuning controller

    P.J. Gawthrop

  • Continuous-time self-tuning control

    P. J. Gawthrop

  • Implementation and application of microprocessor-based self-tuners

    D. W. Clarke;P. J. Gawthrop

  • The frequency of human, manual adjustments in balancing an inverted pendulum is constrained by intrinsic physiological factors

    Ian David Loram;Peter Gawthrop;Martin Lakie

  • Identification of time delays using a polynomial identification method

    P.J. Gawthrop;M.T. Nihtilä

  • Off-equilibrium linearisation and design of gain-scheduled control with application to vehicle speed control

    T.A. Johansen;K.J. Hunt;P.J. Gawthrop;H. Fritz

  • Event-driven intermittent control

    Peter J. Gawthrop;Liuping Wang

  • CONTINUOUS-TIME GENERALIZED PREDICTIVE CONTROL (CGPC)

    P.J. Gawthrop;H. Demircioğlu

  • Control of Time-Delay Systems

    P.J. Gawthrop

Frequent Co-Authors

Liuping Wang
Liuping Wang RMIT University
Wen-Hua Chen
Wen-Hua Chen Loughborough University
Tor Arne Johansen
Tor Arne Johansen Norwegian University of Science and Technology
Eric Rogers
Eric Rogers University of Southampton
David H. Owens
David H. Owens University of Sheffield

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