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.

本文重新审视了系统识别，并说明了在从嘈杂和不平衡数据集进行非线性系统建模的情况下，如何使用机器学习的新范例来改进它。我们表明，在系统识别中使用重要性采样方案可以在建模中显着提高性能，这对预测性控制器很有帮助。首先在无人水面飞行器（USV）的模拟数据上评估该方法的性能。在此数据集上，我们的方法始终优于基线方法。此外，我们在控制环境中展示了这种识别方法的优势。我们在模型预测路径积分（MPPI）控制器中使用无人水面飞行器（USV）的模型来执行跟踪任务。我们讨论了控制器参数的影响，并表明优先模型优于标准方法。最后，我们使用此处开发的专有技术将模型预测路径积分（MPPI）应用于实际系统。