School of Mechanical, Electrical and Manufacturing Engineering


Dr James Fleming Pronouns: He/him MEng Engineering Science (Oxford) DPhil Engineering Science (Oxford)

Photo of Dr James Fleming

Lecturer in Control and Optimisation

James Fleming is a Lecturer within the Wolfson School of Mechanical, Electrical and Manufacturing Engineering at Loughborough University, joining the school in September 2019.

James obtained the MEng and DPhil degrees in 2012 and 2016 respectively from the University of Oxford, where he studied control engineering and developed algorithms for Model Predictive Control of uncertain state-space systems as part of his doctoral research. From 2016 to 2019 he was a Research Fellow at the University of Southampton, developing driver models and optimal control algorithms for the G-Active (Green, Adaptive ConTrol of Interconnected VEhicles) project, which used knowledge of driver preferences to save fuel and reduce emissions in the energy management of conventional, hybrid and electric vehicles.


Doctor of Philosophy in Engineering Science

Master of Engineering Science

Optimal control of constrained and uncertain systems

Many important control problems in industry involve optimising a performance measure such as cost or energy consumption of a process while satisfying constraints, which may be safety critical. But real-world control systems must deal with uncertainty in the controlled process, giving satisfaction of constraints and good performance in all or almost all cases.

Advanced modelling and control techniques have great potential to improve energy economy and safety in the automotive and renewable energy sectors. This is especially true given the recent pushes towards electrification of powertrains and incorporation of driver assistance systems in cars, with all new cars in the UK planned to be zero emission by 2040, and several forms of safety-related driver assistance such as autonomous emergency braking, lane-keeping assistance, and intelligent speed assistance being mandatory in new vehicles within the EU from 2022. In the renewable energy sector, advanced control can improve the lifespan and output of wind turbines and wind farms.


Model predictive control and its applications

Model Predictive Control (MPC) forms a powerful framework for constrained control in which the state of the system is predicted and optimised over some future horizon, but work is still ongoing to develop algorithms that account for uncertainty. Naive approaches typically have a computational complexity that grows exponentially as predictions are made further into the future, making them unusable. To remedy this, we have developed approaches that consider sets of predicted states rather than the states themselves. This adds a little conservatism but leads to controllers that work very well in practice, with many applications throughout the automotive, electrical power and renewable energy industries.

Some recent examples are the design of driver assistance and autonomous driving systems that use modelling and prediction of conventional and electrified powertrains and upcoming traffic conditions to reduce energy usage when following other vehicles, intelligent vehicle air conditioning that uses less fuel by scheduling use of a compressor when the combustion engine is operating at high efficiency, and gyroscopic stabilisation systems for motorcycles that enhance stability and handling during cornering and under heavy braking.

WSB045, Electrical Power and Machines (Module Leader)
WSC104, Robotics and Control (Module Leader)

Lecturer on:

TTD108 / WSD522 / WSP022 - Power Electronics, Machines and Drives
WSD527 / WSP027 - Advanced Methods for Control

Selected publications:

  • J. M. Fleming, C. A. Allison, X. Yan, N. A. Stanton and R. Lot
    Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data
    Safety Science, 2019

  • J. Fleming and M. Cannon
    Stochastic MPC for additive and multiplicative uncertainty using sample approximations
    IEEE Transactions on Automatic Control, 2018

  • R. Lot and J. Fleming
    Gyroscopic stabilisers for powered two-wheeled vehicles
    Vehicle System Dynamics, 2018

  • J. Fleming, B. Kouvaritakis and M. Cannon
    Robust tube MPC for linear systems with multiplicative uncertainty
    IEEE Transactions on Automatic Control, 2015

Recent Publications:

  • C. K. Allison, J. M. Fleming, X. Yan, R. Lot, and N. A. Stanton
    Assisted Eco-Driving: A Practical Guide to the Design and Testing of an Eco-Driving Assistance System (EDAS)
    CRC Press, 2021
  • J. M. Fleming, C. A. Allison, X. Yan, N. A. Stanton and R. Lot
    The benefit of assisted and unassisted eco-driving for electrified powertrains
    IEEE Transactions on Human-Machine Systems, 2021
  • Q. Luo, A. T. Nguyen, J. Fleming, H. Zhang
    Unknown input observer based approach for distributed tube-based Model Predictive Control of heterogeneous vehicle platoons
    IEEE Transactions on Vehicular Technology, 2021
  • J. M. Fleming, C. A. Allison, X. Yan, N. A. Stanton and R. Lot
    Incorporating driver preferences into eco-driving assistance systems using optimal control
    IEEE Transactions on Intelligent Transportation Systems, 2020

Athena Swan Bronze award

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The Wolfson School of Mechanical, Electrical and Manufacturing Engineering
Loughborough University
LE11 3TU