Many vehicle control systems depend on the body sideslip angle, but robust and cost-effective direct measurement of this angle is yet to be achieved for production vehicles. Estimation from indirect measurements is thus the only viable option. In the paper, a sideslip estimator is obtained through the identification of a linear parameter varying (LPV) model. Although inspired by physical insights into the vehicle lateral dynamics, the structure of the LPV estimator is not parametrized beforehand. Instead, the estimator is learned by means of a state-of-the-art non-parametric method for linear parameter varying identification, namely least-squares support vector machines (LS-SVM). Its performance is assessed over an extensive and heterogeneous set of experimental data, showing the effectiveness of the proposed estimator.

许多车辆控制系统取决于车身侧滑角，但是对于量产车辆，仍需要实现该角度的鲁棒且经济高效的直接测量。因此，间接测量估算是唯一可行的选择。在本文中，通过识别线性参数变化（LPV）模型获得了侧滑估计量。尽管LPV估算器的结构是从对车辆横向动力学的物理洞察力启发而来的，但并未事先进行参数设置。取而代之的是，通过用于线性参数变化识别的最新非参数方法（即最小二乘支持向量机（LS-SVM））来学习估算器。它的性能是通过广泛而异类的实验数据集进行评估的，表明了拟议估算器的有效性。