James Fleming

Dr. James Fleming

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Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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8 July 2014 in IEEE Transactions on Automatic Control
Verified
Journal Article
Closed access
JFJames Fleming
James Fleming
BKBasil Kouvaritakis
Basil Kouvaritakis
MCMark Cannon
Mark Cannon
Recently, a technical note [4] ensured recursive feasibility in robust model predictive control with multiplicative uncertainty by applying terminal constraints to the degrees of freedom available to the controller while constructing polytopic tube cross sections. The current note extends this technique by bounding the tube parameters in norm and constructing a terminal set based on this bound. This reduces the online computational load and so allows longer prediction horizons to be used.

9 March 2021 in IEEE Transactions on Vehicular Technology
Verified
Journal Article
Open access
QLQianyue Luo
Qianyue Luo
Anh-Tu NguyenAnh-Tu Nguyen
Anh-Tu Nguyen
JFJames Fleming
James Fleming
FZFeifei Zhang
Feifei Zhang
This paper addresses the control problem of heterogeneous vehicle platoons subject to disturbances and modeling errors. The objective is to guarantee spatial-geometry constraints of vehicles in a platoon. We deal with the case where a predecessor-leader following (PLF) communication topology is used and heterogeneous vehicle dynamics is subject to disturbances. To estimate the lumped disturbance, the technique of unknown input proportional multiple-integral (PMI) observer is employed such that both the state and the disturbance are simultaneously estimated. Moreover, tube-based model predictive control (TMPC) is used and the corresponding control law is composed of a feed-forward term, a feedback term, and a disturbance compensation term. The gains in the integrated control strategy are optimized by utilizing the particle swarm optimization (PSO) algorithm with an H ∞ performance index of an augmented error system. It is proved that the deviations between the actual system and the nominal system are bounded in a robustly positively invariant (RPI) set, that is, the main objective is guaranteed. With the proposed control strategy, simulations and comparisons are carried out. We can see that the control performance of the proposed strategy is significantly improved while the computational time is reduced compared with existing methods.

1 November 2019 in Safety Science
Verified
Journal Article
Closed access
JFJames Fleming
James Fleming
Craig Kevin AllisonCraig Kevin Allison
Craig Kevin Allison
XYXingda Yan
Xingda Yan
RLRoberto Lot
Roberto Lot
NSNeville A Stanton
Neville A Stanton

28 April 2022 in Renewable Energy
Verified
Journal Article
Open access
QHQusay Hawari
Qusay Hawari
TKTaeseong Kim
Taeseong Kim
CWChristopher Ward
Christopher Ward
JFJames Fleming
James Fleming

9 March 2020 in IEEE Transactions on Intelligent Transportation Systems
Verified
Journal Article
Closed access
JFJames Fleming
James Fleming
XYXingda Yan
Xingda Yan
RLRoberto Lot
Roberto Lot
Recently there have been several proposals for `eco-driving assistance systems', designed to save fuel or electrical power by encouraging behaviours such as gentle acceleration and coasting to a stop. These systems use optimal control to find driving behaviour that minimises vehicle energy losses. In this paper, we introduce a methodology to account for driver preferences on acceleration, braking, following distances and cornering speed in such eco-driving optimal control problems. This consists of an optimal control model of acceleration and braking behaviour containing several physically-meaningful parameters to describe driver preferences. If used in combination with a model of fuel or energy consumption, this can provide an adjustable trade-off between satisfying those preferences and minimising energy losses. We demonstrate that the model gives comparable performance to existing car-following and cornering models when predicting drivers' speed in these situations by comparison with real-world driving data. Finally, we present an example highway braking scenario for an electric vehicle, illustrating a trade-off between satisfying driver preferences on vehicle speed and acceleration and reducing electrical energy usage by up to 43%.

19 July 2018 in Vehicle System Dynamics
Verified
Journal Article
Closed access
RLRoberto Lot
Roberto Lot
JFJames Fleming
James Fleming
This paper illustrates the potential of a gyroscopic stabiliser for the stabilisation of single-track vehicles, at low and high speed as well as during braking. Alternative systems are considered, including single and twin counter-rotating gyroscopes, spinning and precessing with respect to different axes, either freely (passive stabilisers) or in a controlled way (active stabilisers). A suitable mathematical model has been developed and stability has been investigated both by eigenvalue calculation and time domain simulations. It has been found that the most effective configuration is one where the gyroscope(s) spin with respect to an axis parallel to the wheels' spin axis and swing with respect to the vehicle yaw axis. Passive systems may effectively stabilise both weave and wobble at medium and high speed, but cannot stabilise the vehicle at low and zero speed. On the contrary, actively controlled gyroscopes are capable of stabilising the vehicle in its whole range of operating speed, as well as during braking. The alteration of the original vehicle handling characteristics is negligible when active counter-rotating gyroscopes are used, and still acceptable if a single gyroscope is adopted instead.