Downsampling for Efficient Tuning of Nonlinear Model Predictive Control Motion Cueing Algorithm

@inproceedings{Gonzalez2024,
title = {Downsampling for Efficient Tuning of Nonlinear Model Predictive Control Motion Cueing Algorithm},
author = {Camilo Gonzalez and Lars Kooijman and Chee Peng Lim and Houshyar Asadi},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet and Stéphane Espié},
isbn = {978-2-9573777-5-6},
year  = {2024},
date = {2024-09-18},
urldate = {2024-09-18},
booktitle = {Proceedings of the Driving Simulation Conference 2024 Europe VR},
pages = {103-110},
address = {Strasbourg, France},
organization = {Driving Simulation Association},
abstract = {Motion Cueing Algorithms (MCAs) encode the movement of simulated vehicles for reproduction with a motion simulator, aiming to provide a realistic driving experience within the capabilities of the machine. Herein, a new approach based on plant downsampling is proposed to accelerate optimisation-based tuning of a state-of-the art MCA for serial robot simulators based on nonlinear model predictive control. The tuning procedure uses particle swarm optimisation. The acceleration method consists in solving the tuning task using a downsampled version of the MCA and then transferring the results to the original version of the MCA. Using Python implementations of the MCA and tuning problem, it is shown through simulation experiments and statistical analyses that the proposed downsampling approach produces sets of tuning parameters that are transferable, without loss of performance, to the original MCA. It is also shown that given the same computational time and resources, the proposed method outperforms or in the worst case matches the performance of the conventional tuning approach on unseen motion cueing scenarios 90.5% of the time. Overall, the method enables searching for desired MCA behaviours through tuning at least ten times faster than previously possible.},
keywords = {},
}

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TY  - CONF
TI  - Downsampling for Efficient Tuning of Nonlinear Model Predictive Control Motion Cueing Algorithm
AU  - Gonzalez, Camilo
AU  - Kooijman, Lars
AU  - Lim, Chee Peng
AU  - Asadi, Houshyar
C1  - Strasbourg, France
C3  - Proceedings of the Driving Simulation Conference 2024 Europe VR
DA  - 2024/09/18
PY  - 2024
SP  - 103
EP  - 110
LA  - en-US
PB  - Driving Simulation Association
SN  - 978-2-9573777-5-6
L2  - https://proceedings.driving-simulation.org/proceeding/dsc-2024/downsampling-for-efficient-tuning-of-nonlinear-model-predictive-control-motion-cueing-algorithm
ER  - 

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