论文标题
基于模型的vs数据驱动的车辆侧滑角度估计和轮胎力测量的益处
Model-based vs Data-driven Estimation of Vehicle Sideslip Angle and Benefits of Tyre Force Measurements
论文作者
论文摘要
本文提供了基于模型和数据驱动的方法的全面比较，并分析了使用测量的轮胎对车辆侧滑角估计的好处。基于模型的方法基于扩展的Kalman滤波器和一个无知的Kalman滤波器，其中测量的轮胎力在观察模型中使用。引入了自适应协方差矩阵，以最大程度地减少回避操纵期间的轮胎模型不匹配。对于数据驱动的方法，评估了饲料向前和复发性神经网络。两种方法都使用标准的惯性测量单元和轮胎力测量作为输入。使用216个操作的大规模实验数据集，我们使用数据驱动与基于模型的方法证明了准确性的显着提高。轮胎力量测量改善了基于模型和数据驱动的方法的性能，尤其是在非线性轮胎方面。
This paper provides a comprehensive comparison of model-based and data-driven approaches and analyses the benefits of using measured tyre forces for vehicle sideslip angle estimation. The model-based approaches are based on an extended Kalman filter and an unscented Kalman filter, in which the measured tyre forces are utilised in the observation model. An adaptive covariance matrix is introduced to minimise the tyre model mismatch during evasive manoeuvres. For data-driven approaches, feed forward and recurrent neural networks are evaluated. Both approaches use the standard inertial measurement unit and the tyre force measurements as inputs. Using the large-scale experimental dataset of 216 manoeuvres, we demonstrate a significant improvement in accuracy using data-driven vs. model-based approaches. Tyre force measurements improve the performance of both model-based and data-driven approaches, especially in the non-linear regime of tyres.
