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Trajectory-control using deep system identification and model predictive control for drone control under uncertain load

Abstract : Machine learning allows to create complex models if provided with enough data, hence challenging more traditional system identification methods. We compare the quality of neural networks and an ARX model when use in an model predictive control to command a drone in a simulated environment. The training of neural networks can be challenging when the data is scarce or datasets are unbalanced. We propose an adaptation of prioritized replay to system identification in order to mitigate these problems. We illustrate the advantages and limits of this training method on the control task of a simulated drone.
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https://hal.archives-ouvertes.fr/hal-01927035
Contributor : Antoine Mahé <>
Submitted on : Monday, November 19, 2018 - 3:41:08 PM
Last modification on : Friday, April 2, 2021 - 3:37:03 AM
Long-term archiving on: : Wednesday, February 20, 2019 - 3:15:35 PM

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Antoine Mahé, Cédric Pradalier, Matthieu Geist. Trajectory-control using deep system identification and model predictive control for drone control under uncertain load. 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), Oct 2018, Sinaia, Romania. ⟨10.1109/ICSTCC.2018.8540719⟩. ⟨hal-01927035⟩

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