'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
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
