Model Predictive Motion Cueing: Online Prediction and Washout Tuning
Authors:
Frank M. Drop, Mario Olivari, Mikhail Katliar, Heinrich H. Bülthoff
Keywords:
motion cueing, model predictive control, optimization-based, online prediction method
Abstract:
A Motion Cueing Algorithm (MCA) based on Model Predictive Control (MPC) plans a simulator trajectory that optimally satisfies a user-defined cost function, whilst not exceeding the simulator limits, given a prediction of the future specific forces and rotational rates (inertial signals). Model Predictive Motion Cueing Algorithms (MPMCAs) have important advantages over filter-based MCAs, e.g., they allow for zero phase distortion filtering. It is, however, not clear how the inertial signals should be predicted and how accurate the prediction needs to be to obtain an acceptable quality of perceived motion. Furthermore, it is not clear how to prevent discrepancies in the predicted inertial signals to cause large false or missing cues. This paper presents a simple online prediction method and demonstrates how the state-error term of the cost function can be used to reduce the negative effects of prediction errors. The results of a human-in-the-loop experiment are encouraging and informative for further improvement of the method.
Cite this article
Drop F.M.; Olivari M.; Katliar M. and Bülthoff H.H. Model Predictive Motion Cueing: Online Prediction and Washout Tuning In: Proceedings of the Driving Simulation Conference 2018 Europe VR, Driving Simulation Association, Antibes, France, 2018, pp. 71-78
@inproceedings{Drop2018,
title = {Model Predictive Motion Cueing: Online Prediction and Washout Tuning},
author = {Frank M. Drop and Mario Olivari and Mikhail Katliar and Heinrich H. Bülthoff},
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 = {71-78},
address = {Antibes, France},
organization = {Driving Simulation Association},
abstract = {A Motion Cueing Algorithm (MCA) based on Model Predictive Control (MPC) plans a simulator trajectory that optimally satisfies a user-defined cost function, whilst not exceeding the simulator limits, given a prediction of the future specific forces and rotational rates (inertial signals). Model Predictive Motion Cueing Algorithms (MPMCAs) have important advantages over filter-based MCAs, e.g., they allow for zero phase distortion filtering. It is, however, not clear how the inertial signals should be predicted and how accurate the prediction needs to be to obtain an acceptable quality of perceived motion. Furthermore, it is not clear how to prevent discrepancies in the predicted inertial signals to cause large false or missing cues. This paper presents a simple online prediction method and demonstrates how the state-error term of the cost function can be used to reduce the negative effects of prediction errors. The results of a human-in-the-loop experiment are encouraging and informative for further improvement of the method.},
keywords = {model predictive control, motion cueing, online prediction method, optimization-based},
}
TY - CONF TI - Model Predictive Motion Cueing: Online Prediction and Washout Tuning AU - Drop, Frank M AU - Olivari, Mario AU - Katliar, Mikhail AU - Bülthoff, Heinrich H C1 - Antibes, France C3 - Proceedings of the Driving Simulation Conference 2018 Europe VR DA - 2018/09/05 PY - 2018 SP - 71 EP - 78 LA - en-US PB - Driving Simulation Association SN - 978-2-85782-734-4 L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2018/model-predictive-motion-cueing-online-prediction-and-washout-tuning ER -
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