Twin-in-the-loop state estimation for vehicle dynamics control: Theory and experiments

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS IFAC Journal of Systems and Control Pub Date : 2024-07-20 DOI:10.1016/j.ifacsc.2024.100274
Giorgio Riva, Simone Formentin, Matteo Corno, Sergio M. Savaresi
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Abstract

In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile or even not available. Therefore, real-time estimation techniques need to be used. The previous approach suffers from two main drawbacks: (i) the approximations due to model mismatch might jeopardize the performance of the final estimation-based control; (ii) each new estimator requires the calibration from scratch of a dedicated model. In this paper, we propose a twin-in-the-loop scheme, where the ad-hoc model is replaced by an accurate full-fledged simulator of the vehicle, typically available to vehicles manufacturers and suitable for the estimation of any on-board variable, coupled with a compensator within a closed-loop observer scheme. Given the black-box nature of the digital twin, a data-driven methodology for observer tuning is developed, based on Bayesian optimization. The effectiveness of the proposed estimation method for the estimation of vehicle states and forces, as compared to traditional model-based Kalman filtering, is experimentally shown on a dataset collected with a sport car. In summary, the proposed approach achieves, in the most aggressive driving scenarios, an average improvement of 0.5° for the side-slip angle estimation, and of more than 500 N for the front lateral forces estimation.

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用于车辆动力学控制的双在环状态估计:理论与实验
在车辆动力学控制中,许多相关变量无法直接测量,因为传感器可能成本高、易损坏,甚至无法使用。因此,需要使用实时估算技术。以往的方法有两个主要缺点:(i) 模型不匹配导致的近似可能会影响最终基于估计的控制性能;(ii) 每个新的估计器都需要从头校准一个专用模型。在本文中,我们提出了一种 "双环 "方案,即用精确的车辆完整模拟器取代临时模型,该模拟器通常可供车辆制造商使用,适用于任何车载变量的估算,并与闭环观测器方案中的补偿器相结合。考虑到数字孪生系统的黑箱性质,基于贝叶斯优化,开发了一种数据驱动的观测器调整方法。与传统的基于模型的卡尔曼滤波法相比,所提出的估算方法对车辆状态和力的估算效果显著。总之,在最激烈的驾驶情况下,所提出的方法在侧滑角估计方面平均提高了 0.5°,在前侧向力估计方面平均提高了 500 N 以上。
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来源期刊
IFAC Journal of Systems and Control
IFAC Journal of Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
3.70
自引率
5.30%
发文量
17
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