The vehicle sideslip angle is an important state of vehicle lateral dynamics and its knowledge is crucial for the successful implementation of advanced driver-assistance systems. Measuring the vehicle sideslip angle on a production vehicle is challenging because of the exorbitant price of a physical sensor. This paper aims to present a novel framework for virtually sensing/estimating the vehicle sideslip angle. The desired level of accuracy for the estimator is to be within +/− 0.2 degree of the actual sideslip angle of the vehicle. This will make the precision of the proposed estimator at par with expensive commercially available sensors used for physically measuring the vehicle sideslip angle.,The proposed estimator uses an adaptive tire model in conjunction with a model-based observer. The performance of the estimator is evaluated through experimental tests on a rear-wheel drive vehicle.,Detailed experimental results show that the developed system can reliably estimate the vehicle sideslip angle during both steady state and transient maneuvers, within the desired accuracy levels.,This paper presents a novel framework for vehicle sideslip angle estimation. The presented framework combines an adaptive tire model, an unscented Kalman filter-based axle force observer and data from tire mounted sensors. Tire model adaptation is achieved by making extensions to the magic formula, by accounting for variations in the tire inflation pressure, load, tread-depth and temperature. Predictions with the adapted tire model were validated by running experiments on the Flat-Trac® machine. The benefits of using an adaptive tire model for sideslip angle estimation are demonstrated through experimental tests. The performance of the observer is satisfactory, in both transient and steady state maneuvers. Future work will focus on measuring tire slip angle and road friction information using tire mounted sensors and using that information to further enhance the robustness of the vehicle sideslip angle observer.

中文翻译：

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用于实时估计车辆侧滑角的虚拟传感器

车辆侧滑角是车辆横向动力学的重要状态，其知识对于成功实施先进的驾驶员辅助系统至关重要。由于物理传感器的价格过高，在量产车辆上测量车辆侧滑角具有挑战性。本文旨在提出一种用于虚拟感测/估计车辆侧滑角的新框架。估计器的期望精度水平应在车辆实际侧滑角的 +/- 0.2 度以内。这将使提议的估计器的精度与用于物理测量车辆侧滑角的昂贵的商用传感器相当。提议的估计器结合基于模型的观察器使用自适应轮胎模型。估计器的性能通过后轮驱动车辆的实验测试进行评估。详细的实验结果表明，开发的系统可以在所需的精度水平内可靠地估计稳态和瞬态操纵期间的车辆侧滑角。,这论文提出了一种新的车辆侧滑角估计框架。所提出的框架结合了自适应轮胎模型、基于无味卡尔曼滤波器的轴力观测器和来自轮胎安装传感器的数据。通过对神奇公式进行扩展，通过考虑轮胎充气压力、负载、胎面深度和温度的变化来实现轮胎模型适应。通过在 Flat-Trac® 机器上运行实验，验证了对适应轮胎模型的预测。通过实验测试证明了使用自适应轮胎模型进行侧滑角估计的好处。在瞬态和稳态机动中，观察者的表现都令人满意。未来的工作将集中在使用安装在轮胎上的传感器测量轮胎侧偏角和道路摩擦信息，并使用这些信息进一步增强车辆侧滑角观测器的鲁棒性。