40 research outputs found
Robust Model Predictive Control Framework for Energy-Optimal Adaptive Cruise Control of Battery Electric Vehicles
The autonomous vehicle following problem has
been extensively studied for at least two decades with the rapid
development of intelligent transport systems. In this context,
this paper proposes a robust model predictive control (RMPC)
method that aims to find the energy-efficient following velocity
of an ego battery electric vehicle and to guarantee a safe rearend distance in the presence of disturbances and modelling
errors. The optimisation problem is formulated in the space
domain so that the overall problem can be convexified in
the form of a semi-definite program, which ensures a rapid
solving speed and a unique solution. Simulations are carried
out to provide numerical comparisons with a nominal model
predictive control (MPC) scheme. It is shown that the RMPC
guarantees robust constraint satisfaction for the closed-loop
system whereas constraints may be violated when the nominal
MPC is in use. Moreover, the impact of the prediction horizon
length on optimality is investigated, showing that a finely tuned
horizon could produce significant energy savings
A Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles
The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach
A Computationally Efficient Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles
The recent advancement in vehicular networking technology provides novel
solutions for designing intelligent and sustainable vehicle motion controllers.
This work addresses a car-following task, where the feedback linearisation
method is combined with a robust model predictive control (RMPC) scheme to
safely, optimally and efficiently control a connected electric vehicle. In
particular, the nonlinear dynamics are linearised through a feedback
linearisation method to maintain an efficient computational speed and to
guarantee global optimality. At the same time, the inevitable model mismatch is
dealt with by the RMPC design. The control objective of the RMPC is to optimise
the electric energy efficiency of the ego vehicle with consideration of a
bounded model mismatch disturbance subject to satisfaction of physical and
safety constraints. Numerical results first verify the validity and robustness
through a comparison between the proposed RMPC and a nominal MPC. Further
investigation into the performance of the proposed method reveals a higher
energy efficiency and passenger comfort level as compared to a recently
proposed benchmark method using the space-domain modelling approach.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
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Dynamic Analysis of Double Wishbone Front Suspension Systems for Sport Motorcycles
In this paper, two alternative front suspension systems and their influence on motorcycle dynamics are investigated. Based on an existing motorcycle mathematical model, the front end is modified to accommodate both Girder and Hossack suspension systems. Both of them have in common a double wishbone design that varies the front end geometry on certain manoeuvrings and, consequently, the machine’s behaviour. The kinematics of the two systems and their impact on the motorcycle performance is analysed and compared to the well known telescopic fork suspension system. Stability study for both systems is carried out by means of root-loci methods, in which the main oscillation modes, weave and wobble, are studied and compared to the baseline model
A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing
Cooperative vehicle management emerges as a promising solution to improve
road traffic safety and efficiency. This paper addresses the speed planning
problem for connected and autonomous vehicles (CAVs) at an unsignalized
intersection with consideration of turning maneuvers. The problem is approached
by a hierarchical centralized coordination scheme that successively optimizes
the crossing order and velocity trajectories of a group of vehicles so as to
minimize their total energy consumption and travel time required to pass the
intersection. For an accurate estimate of the energy consumption of each CAV,
the vehicle modeling framework in this paper captures 1) friction losses that
affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line
with a battery-electric architecture. It is shown that the underlying
optimization problem subject to safety constraints for powertrain operation,
cornering and collision avoidance, after convexification and relaxation in some
aspects can be formulated as two second-order cone programs, which ensures a
rapid solution search and a unique global optimum. Simulation case studies are
provided showing the tightness of the convex relaxation bounds, the overall
effectiveness of the proposed approach, and its advantages over a benchmark
solution invoking the widely used first-in-first-out policy. The investigation
of Pareto optimal solutions for the two objectives (travel time and energy
consumption) highlights the importance of optimizing their trade-off, as small
compromises in travel time could produce significant energy savings.Comment: 16 pages, 11 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl