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Journal Articles Journal of Intelligent & Robotic Systems Year : 2021

Evaluation of prioritized deep system identification on a path following task

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Antoine Mahé
Antoine Richard
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  • PersonId : 1087046
Matthieu Geist
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  • PersonId : 790158
  • IdRef : 142341819
Cedric Pradalier

Abstract

This paper revisits system identification and shows how new paradigms from machine learning can be used to improve it in the case of non-linear systems modeling from noisy and unbalanced dataset. We show that using importance sampling schemes in system identification can provide a significant performance boost in modeling, which is helpful to a predictive controller. The performance of the approach is first evaluated on simulated data of a Unmanned Surface Vehicle (USV). Our approach consistently outperforms baseline approaches on this dataset. Moreover we demonstrate the benefits of this identification methodology in a control setting. We use the model of the Unmanned Surface Vehicle (USV) in a Model Predictive Path Integral (MPPI) controller to perform a track following task. We discuss the influence of the controller parameters and show that the prioritized model outperform standard methods. Finally, we apply the Model Predictive Path Integral (MPPI) on a real system using the know-how developed here.
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Dates and versions

hal-03138279 , version 1 (11-02-2021)

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Antoine Mahé, Antoine Richard, Stéphanie Aravecchia, Matthieu Geist, Cedric Pradalier. Evaluation of prioritized deep system identification on a path following task. Journal of Intelligent & Robotic Systems , 2021, ⟨10.1007/s10846-021-01341-1⟩. ⟨hal-03138279⟩
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