Mandl, P., Jaumann, F., Unterreiner, M., Gräber, T., Klinger, F., Edelmann, J., & Plöchl, M. (2024). Speed Control in the Presence of Road Obstacles: A Comparison of Model Predictive Control and Reinforcement Learning. In 16th International Symposium on Advanced Vehicle Control (pp. 91–97). CRC Press. https://doi.org/10.1007/978-3-031-70392-8_14
E325-01 - Forschungsbereich Technische Dynamik und Fahrzeugdynamik
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Published in:
16th International Symposium on Advanced Vehicle Control
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Date (published):
2024
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Event name:
16th International Symposium on Advanced Vehicle Control - AVEC' 24
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Event date:
2-Sep-2024 - 6-Sep-2024
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Event place:
Mailand, Italy
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Number of Pages:
7
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Publisher:
CRC Press
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Peer reviewed:
Yes
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Keywords:
Longitudinal Control; Model Predictive Control; Reinforcement Learning; Ride Comfort
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Abstract:
The paper compares two optimal control methods — Reinforcement Learning and Model Predictive Control — for adaptive speed control in the presence of road obstacles to enhance ride comfort. Both methods use a model for training or prediction and a reward or cost function to achieve a desired control objective. Using the same quarter-car model and objective function for both methods, differences in planned speed profiles, optimality of the control objective, and differences in computational time are analysed through simulations over a series of cosine-shaped road bumps.
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Research Areas:
Sustainable and Low Emission Mobility: 25% Information Systems Engineering: 25% Modeling and Simulation: 50%
CC BY 4.0
