World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
41
Citations
7421
World Ranking
4274
National Ranking
224

Overview

Mark Cannon is affiliated with the University of Oxford in the United Kingdom, specializing in the field of engineering with a significant focus on control and systems engineering. Their work spans several subfields including automotive engineering, electrical and electronic engineering, aerospace engineering, and computational theory and mathematics.

Their research covers a range of topics that include:

  • Advanced Control Systems Optimization
  • Control Systems and Identification
  • Fault Detection and Control Systems
  • Advanced Battery Technologies Research
  • Electric and Hybrid Vehicle Technologies
  • Electric Vehicles and Infrastructure
  • Process Optimization and Integration

Mark Cannon has contributed to numerous peer-reviewed papers. Notable recent publications include:

  • Fast Self-Triggered MPC for Constrained Linear Systems With Additive Disturbances, 2020, IEEE Transactions on Automatic Control
  • Predictive Energy Management for Hybrid Electric Aircraft Propulsion Systems, 2022, IEEE Transactions on Control Systems Technology
  • Robust adaptive model predictive control: Performance and parameter estimation, 2020, International Journal of Robust and Nonlinear Control
  • Scenario Model Predictive Control for Data-Based Energy Management in Plug-In Hybrid Electric Vehicles, 2022, IEEE Transactions on Control Systems Technology
  • Robust adaptive model predictive control with persistent excitation conditions, 2023, Automatica

Their frequent coauthors include Martin Doff-Sotta, Shuhao Yan, Marko Bacic, Paul J. Goulart, and Yana Lishkova, reflecting a collaborative research network.

Mark Cannon's work has been published across a variety of venues, with repeated contributions to:

  • arXiv (Cornell University)
  • IFAC-PapersOnLine
  • IEEE Transactions on Automatic Control
  • IEEE Transactions on Control Systems Technology
  • Automatica

Best Publications

  • Failing to Learn and Learning to Fail (Intelligently): How Great Organizations Put Failure to Work to Innovate and Improve

    Mark D. Cannon;Amy C. Edmondson

  • Confronting Failure: Antecedents and Consequences of Shared Beliefs About Failure in Organizational Work Groups

    Mark D. Cannon;Amy C. Edmondson

  • Stochastic Tubes in Model Predictive Control With Probabilistic Constraints

    Mark Cannon;Basil Kouvaritakis;Saša V Raković;Qifeng Cheng

  • Model Predictive Control

    Unknown

  • Model Predictive Control: Classical, Robust and Stochastic

    Basil Kouvaritakis;Mark Cannon

  • Nonlinear predictive control : theory and practice

    Basil Kouvaritakis;Mark Cannon

  • Homothetic tube model predictive control

    SašA V. Raković;Basil Kouvaritakis;Rolf Findeisen;Mark Cannon

  • Probabilistic Constrained MPC for Multiplicative and Additive Stochastic Uncertainty

    M. Cannon;B. Kouvaritakis;Xingjian Wu

  • Efficient nonlinear model predictive control algorithms

    Mark Cannon

  • Actionable feedback: Unlocking the power of learning and performance improvement

    Mark D. Cannon;Robert Witherspoon

  • Parameterized Tube Model Predictive Control

    S. V. Rakovic;B. Kouvaritakis;M. Cannon;C. Panos

  • Brief paper: Explicit use of probabilistic distributions in linear predictive control

    Basil Kouvaritakis;Mark Cannon;Saša V. Raković;Qifeng Cheng

  • Robust MPC with recursive model update

    Matthias Lorenzen;Mark Cannon;Frank Allgöwer

  • Explicit use of probabilistic distributions in linear predictive control

    Basil Kouvaritakis;Mark Cannon;Saša V. Raković;Qifeng Cheng

  • The Kaposi's sarcoma-associated herpesvirus G protein-coupled receptor has broad signaling effects in primary effusion lymphoma cells.

    Mark Cannon;Nicola J. Philpott;Ethel Cesarman

  • Optimizing prediction dynamics for robust MPC

    M. Cannon;B. Kouvaritakis

  • Space-frequency localized basis function networks for nonlinear system estimation and control

    Mark Cannon;Jean-Jacques E. Slotine

  • Technical Communique: Who needs QP for linear MPC anyway?

    B. Kouvaritakis;M. Cannon;J. A. Rossiter

  • Robust Tube MPC for Linear Systems With Multiplicative Uncertainty

    James Fleming;Basil Kouvaritakis;Mark Cannon

  • Brief Nonlinear model predictive control with polytopic invariant sets

    M. Cannon;V. Deshmukh;B. Kouvaritakis

  • Brief paper: Stochastic tube MPC with state estimation

    Mark Cannon;Qifeng Cheng;Basil Kouvaritakis;Saša V. Raković

  • Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

    Mark Cannon;Basil Kouvaritakis;Xingjian Wu

  • Nonlinear model predictive control with polytopic invariant sets

    M. Cannon;V. Deshmukh;B. Kouvaritakis

Frequent Co-Authors

Basil Kouvaritakis
Basil Kouvaritakis University of Oxford
Rolf Findeisen
Rolf Findeisen Technical University of Darmstadt
Frank Allgöwer
Frank Allgöwer University of Stuttgart
Michael J. Grimble
Michael J. Grimble University of Strathclyde

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For those pursuing Electronics and Electrical Engineering in the USA, exploring related online degrees can provide flexible opportunities to advance your education and career. Many students benefit from competency based masters, which focus on skill mastery rather than time, enabling faster completion and practical learning tailored to your career goals.

Military spouses and dependents often face unique challenges when pursuing education. The best online college for military spouses offers tailored programs and support to accommodate frequent relocations, ensuring continuous progress in your studies without interruption.

Flexibility is key for many students, especially those balancing work or family. Institutions with online colleges with weekly start dates allow you to begin your coursework any week, removing barriers often caused by traditional semester schedules.

For professionals seeking quick upskilling, certificate programs that pay well present an excellent option. These short-term programs provide practical credentials that can enhance your employability and earning potential in the engineering field.

Best Scientists Citing Mark Cannon

Trending Scientists