A Model Predictive Motion Cueing Strategy for a 5-Degree-of-Freedom Driving Simulator with Hybrid Kinematics
Authors:
Patrick Biemelt, Sven Henning, Nico Rüddenklau, Sandra Gausemeier, Ansgar Trächtler
Keywords:
motion cueing, model predictive control, dynamic motion platform control, hybrid kinematics
Abstract:
Due to the increasing functionality and complexity of advanced driver assistance systems (ADAS) in the automotive field, efficient tools for development and test purposes of vehicle systems are needed more than ever. In this context, the use of dynamic driving simulators marks a key technology to fill the gap between virtual prototyping and time-consuming road tests. However, the informational value of these virtual test runs depends highly on the appropriate reproduction of the simulated vehicle’s motion. For this reason, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are developed to replicate the acting accelerations and angular velocities within the physical limitations of the driving simulator. In this paper, we present a novel Model Predictive Control (MPC) approach for this task. The proposed control strategy explicitly considers the underlying system dynamics as well as kinematic relations in the numerical optimization process. This feature enables the algorithm to reproduce the vehicle dynamics at any reference point within the driving simulator, for instance the driver’s head position, while considering all relevant kinematic effects of the underlying motion system. The proposed MCA is implemented on a driving simulator with unique hybrid kinematics and validated by measurement data of standard driving scenarios.
Cite this article
Biemelt P.; Henning S.; Rüddenklau N.; Gausemeier S. and Trächtler A. A Model Predictive Motion Cueing Strategy for a 5-Degree-of-Freedom Driving Simulator with Hybrid Kinematics In: Proceedings of the Driving Simulation Conference 2018 Europe VR, Driving Simulation Association, Antibes, France, 2018, pp. 79-85
@inproceedings{Biemelt2018,
title = {A Model Predictive Motion Cueing Strategy for a 5-Degree-of-Freedom Driving Simulator with Hybrid Kinematics},
author = {Patrick Biemelt and Sven Henning and Nico Rüddenklau and Sandra Gausemeier and Ansgar Trächtler},
editor = {Andras Kemeny and Florent Colombet and Frédéric Merienne and Stéphane Espié},
isbn = {978-2-85782-734-4},
year = {2018},
date = {2018-09-05},
booktitle = {Proceedings of the Driving Simulation Conference 2018 Europe VR},
pages = {79-85},
address = {Antibes, France},
organization = {Driving Simulation Association},
abstract = {Due to the increasing functionality and complexity of advanced driver assistance systems (ADAS) in the automotive field, efficient tools for development and test purposes of vehicle systems are needed more than ever. In this context, the use of dynamic driving simulators marks a key technology to fill the gap between virtual prototyping and time-consuming road tests. However, the informational value of these virtual test runs depends highly on the appropriate reproduction of the simulated vehicle’s motion. For this reason, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are developed to replicate the acting accelerations and angular velocities within the physical limitations of the driving simulator. In this paper, we present a novel Model Predictive Control (MPC) approach for this task. The proposed control strategy explicitly considers the underlying system dynamics as well as kinematic relations in the numerical optimization process. This feature enables the algorithm to reproduce the vehicle dynamics at any reference point within the driving simulator, for instance the driver’s head position, while considering all relevant kinematic effects of the underlying motion system. The proposed MCA is implemented on a driving simulator with unique hybrid kinematics and validated by measurement data of standard driving scenarios.},
keywords = {dynamic motion platform control, hybrid kinematics, model predictive control, motion cueing},
}
TY - CONF TI - A Model Predictive Motion Cueing Strategy for a 5-Degree-of-Freedom Driving Simulator with Hybrid Kinematics AU - Biemelt, Patrick AU - Henning, Sven AU - Rüddenklau, Nico AU - Gausemeier, Sandra AU - Trächtler, Ansgar C1 - Antibes, France C3 - Proceedings of the Driving Simulation Conference 2018 Europe VR DA - 2018/09/05 PY - 2018 SP - 79 EP - 85 LA - en-US PB - Driving Simulation Association SN - 978-2-85782-734-4 L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2018/a-model-predictive-motion-cueing-strategy-for-a-5-degree-of-freedom-driving-simulator-with-hybrid-kinematics ER -
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