World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
76
Citations
20303
World Ranking
267
National Ranking
132

Electronics and Electrical Engineering

D-Index
77
Citations
20397
World Ranking
624
National Ranking
278

Research.com Recognitions

  • 2016 - Fellow of the American Society of Mechanical Engineers

Overview

Warren E. Dixon is affiliated with the University of Florida in the United States and has established a significant research presence primarily in engineering and computer science. Their work primarily focuses on control systems and related computational methods, reflecting extensive expertise across various interrelated domains.

The primary fields of study for this researcher include:

  • Engineering
  • Computer Science

Within these broad fields, their contributions span several subfields including:

  • Control and Systems Engineering
  • Biomedical Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications

A detailed look at the main research topics highlights focus areas such as:

  • Adaptive Control of Nonlinear Systems
  • Muscle activation and electromyography studies
  • Adaptive Dynamic Programming Control
  • Advanced Control Systems Optimization
  • Neural Networks and Applications
  • Distributed Control Multi-Agent Systems
  • Neuroscience and Neural Engineering

Dixon's publication record shows frequent contributions to several key journals and conference venues. These include:

  • IEEE Transactions on Automatic Control
  • IEEE Transactions on Control Systems Technology
  • IEEE Control Systems Letters
  • arXiv (Cornell University)
  • Automatica

Among recent papers authored by or involving Dixon, several titles exemplify the focus and themes of their work:

  • "Lyapunov-Based Real-Time and Iterative Adjustment of Deep Neural Networks," 2021, IEEE Control Systems Letters
  • "Lyapunov-Based Control of a Nonlinear Multiagent System With a Time-Varying Input Delay Under False-Data-Injection Attacks," 2021, IEEE Transactions on Industrial Informatics
  • "Lyapunov-Derived Control and Adaptive Update Laws for Inner and Outer Layer Weights of a Deep Neural Network," 2021, IEEE Control Systems Letters
  • "Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults," 2020, Automatica
  • "Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems," 2021, IEEE Control Systems Letters

Frequent co-authors contributing to this body of work include:

  • Omkar Sudhir Patil
  • Brendon C. Allen
  • Emily J. Griffis
  • Kimberly J. Stubbs
  • Max L. Greene

Dixon's research intersects control theory, neural network applications, and adaptive systems, often with a focus on nonlinear system control and optimization techniques. The scientific contributions also extend into biomedical aspects, particularly related to muscle activation and neural engineering.

Recognition of the researcher's professional standing includes being named a Fellow of the American Society of Mechanical Engineers in 2016.

Best Publications

  • A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems

    S. Bhasin;R. Kamalapurkar;M. Johnson;K. G. Vamvoudakis

  • Asymptotic Tracking for Systems With Structured and Unstructured Uncertainties

    P.M. Patre;W. MacKunis;C. Makkar;W.E. Dixon

  • Nonlinear Control of Wheeled Mobile Robots

    Warren E. Dixon;Darren M. Dawson;Erkan Zergeroglu;Aman Behal

  • Adaptive Regulation of Amplitude Limited Robot Manipulators With Uncertain Kinematics and Dynamics

    W.E. Dixon

  • Nonlinear coupling control laws for an underactuated overhead crane system

    Y. Fang;W.E. Dixon;D.M. Dawson;E. Zergeroglu

  • Tracking and regulation control of an underactuated surface vessel with nonintegrable dynamics

    A. Behal;D.M. Dawson;W.E. Dixon;Y. Fang

  • Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure

    P.M. Patre;W. MacKunis;K. Kaiser;W.E. Dixon

  • Lyapunov-Based Tracking Control in the Presence of Uncertain Nonlinear Parameterizable Friction

    C. Makkar;G. Hu;W.G. Sawyer;W.E. Dixon

  • A new continuously differentiable friction model for control systems design

    C. Makkar;W.E. Dixon;W.G. Sawyer;G. Hu

  • Nonlinear Control of Engineering Systems: A Lyapunov-Based Approach

    Warren E. Dixon

  • Repetitive learning control: a Lyapunov-based approach

    W.E. Dixon;E. Zergeroglu;D.M. Dawson;B.T. Costic

  • Adaptive tracking control of a wheeled mobile robot via an uncalibrated camera system

    W.E. Dixon;D.M. Dawson;E. Zergeroglu;A. Behal

  • Homography-based visual servo regulation of mobile robots

    Yongchun Fang;W.E. Dixon;D.M. Dawson;P. Chawda

  • Event-Triggered Control of Multiagent Systems for Fixed and Time-Varying Network Topologies

    Teng-Hu Cheng;Zhen Kan;Justin R. Klotz;John M. Shea

  • Homography-based visual servo tracking control of a wheeled mobile robot

    Jian Chen;W.E. Dixon;M. Dawson;M. McIntyre

  • LaSalle-Yoshizawa Corollaries for Nonsmooth Systems

    Nicholas Fischer;Rushikesh Kamalapurkar;Warren E. Dixon

  • Nonlinear RISE-Based Control of an Autonomous Underwater Vehicle

    Nicholas R. Fischer;Devin Hughes;Patrick Walters;Eric M. Schwartz

  • Concurrent Learning for Parameter Estimation Using Dynamic State-Derivative Estimators

    Rushikesh Kamalapurkar;Benjamin Reish;Girish Chowdhary;Warren E. Dixon

  • Approximate optimal trajectory tracking for continuous-time nonlinear systems

    Rushikesh Kamalapurkar;Huyen Dinh;Shubhendu Bhasin;Warren E. Dixon

  • Fault detection for robot manipulators with parametric uncertainty: a prediction error based approach

    W.E. Dixon;I.D. Walker;D.M. Dawson;J.P. Hartranft

  • Global adaptive output feedback tracking control of robot manipulators

    F. Zhang;D.M. Dawson;M.S. de Queiroz;W.E. Dixon

Frequent Co-Authors

Darren M. Dawson
Darren M. Dawson Methode Electronics
Guoqiang Hu
Guoqiang Hu Nanyang Technological University
Aman Behal
Aman Behal University of Central Florida
Yongchun Fang
Yongchun Fang Nankai University
Andrew R. Teel
Andrew R. Teel University of California, Santa Barbara
Frank L. Lewis
Frank L. Lewis The University of Texas at Arlington
Prabir Barooah
Prabir Barooah University of Florida
Ian D. Walker
Ian D. Walker Clemson University
Kyriakos G. Vamvoudakis
Kyriakos G. Vamvoudakis Georgia Institute of Technology
Naira Hovakimyan
Naira Hovakimyan University of Illinois at Urbana-Champaign

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