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.
|Title of host publication
|Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
|Number of pages
|Published - 2023
|2023 IEEE International Conference on Mechatronics, ICM 2023 - Leicestershire, United Kingdom
Duration: 15 Mar 2023 → 17 Mar 2023
|2023 IEEE International Conference on Mechatronics, ICM 2023
|15/03/23 → 17/03/23
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- driving simulator
- model predictive control
- Motion cueing algorithm