  Driving simulators have been used in the automotive industry for many years
because of their ability to perform tests in a safe, reproducible and
controlled immersive virtual environment. The improved performance of the
simulator and its ability to recreate in-vehicle experience for the user is
established through motion cueing algorithms (MCA). Such algorithms have
constantly been developed with model predictive control (MPC) acting as the
main control technique. Currently, available MPC-based methods either compute
the optimal controller online or derive an explicit control law offline. These
approaches limit the applicability of the MCA for real-time applications due to
online computational costs and/or offline memory storage issues. This research
presents a solution to deal with issues of offline and online solving through a
hybrid approach. For this, an explicit MPC is used to generate a look-up table
to provide an initial guess as a warm-start for the implicit MPC-based MCA.
From the simulations, it is observed that the presented hybrid approach is able
to reduce online computation load by shifting it offline using the explicit
controller. Further, the algorithm demonstrates a good tracking performance
with a significant reduction of computation time in a complex driving scenario
using an emulator environment of a driving simulator.
