Authors:
Carolina Rengifo, Jean-Rémy Chardonnet, Damien Paillot, Hakim Mohellebi, Andras Kemeny
Keywords:
optimization, recurrent neural networks, MPC motion cueing algorithm, real-time simulator, penalty method
Abstract:
Because of the critical timing requirement, one major issue regarding model predictive control-based motion cueing algorithms is the calculation of real-time optimal solutions. In this paper, a continuous-time recurrent neural network-based gradient method is applied to compute the optimal control action in real time for an MPCbased MCA.We demonstrate that by implementing a saturation function for the constraints in the decision variables and a regulation for the energy function in the network, a constrained optimization problem can be solved without using any penalty function. Simulation results are included to compare the proposed approach and substantiate the applicability of recurrent neural networks as a quadratic programming solver. A comparison with another QP solver shows that our method can find an optimal solution much faster and with the same precision.
Rengifo C.; Chardonnet J.-R.; Paillot D.; Mohellebi H. and Kemeny A. Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach In: Proceedings of the Driving Simulation Conference 2018 Europe VR, Driving Simulation Association, Antibes, France, 2018, pp. 63-69
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@inproceedings{Rengifo2018,
title = {Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach},
author = {Carolina Rengifo and Jean-Rémy Chardonnet and Damien Paillot and Hakim Mohellebi and Andras Kemeny},
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 = {63-69},
address = {Antibes, France},
organization = {Driving Simulation Association},
abstract = {Because of the critical timing requirement, one major issue regarding model predictive control-based motion cueing algorithms is the calculation of real-time optimal solutions. In this paper, a continuous-time recurrent neural network-based gradient method is applied to compute the optimal control action in real time for an MPCbased MCA.We demonstrate that by implementing a saturation function for the constraints in the decision variables and a regulation for the energy function in the network, a constrained optimization problem can be solved without using any penalty function. Simulation results are included to compare the proposed approach and substantiate the applicability of recurrent neural networks as a quadratic programming solver. A comparison with another QP solver shows that our method can find an optimal solution much faster and with the same precision.},
keywords = {MPC motion cueing algorithm, optimization, penalty method, real-time simulator, recurrent neural networks},
}
Download .bib file
TY - CONF
TI - Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach
AU - Rengifo, Carolina
AU - Chardonnet, Jean-Rémy
AU - Paillot, Damien
AU - Mohellebi, Hakim
AU - Kemeny, Andras
C1 - Antibes, France
C3 - Proceedings of the Driving Simulation Conference 2018 Europe VR
DA - 2018/09/05
PY - 2018
SP - 63
EP - 69
LA - en-US
PB - Driving Simulation Association
SN - 978-2-85782-734-4
L2 - https://proceedings.driving-simulation.org/proceeding/dsc-2018/solving-the-constrained-problem-in-model-predictive-control-based-motion-cueing-algorithm-with-a-neural-network-approach
ER -
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