通过多个过器的交叉组合,通过使用决策树算法对车辆侧滑角度进行强大的虚拟传感
Gaël P Atheupe1,2, Younesse El Mrhasli2, Ulrich Emabou2
1Renault Group Technical Centre, 1 Avenue. du Golf, 78280 Guyancourt, France.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究引入了一种用于估计车辆侧滑角度的新技术,在各种驾驶条件下提高了精度和可靠性. 机器学习算法通过动态选择当前驾驶环境的最佳过器来确保最佳估计.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 准确估计车辆侧滑角度对于稳定性控制系统至关重要.
- 现有的方法在各种操作条件和系统中断的情况下可能难以稳定.
研究的目的:
- 开发一种强大的,高精度的车辆侧滑角度状态估计技术.
- 确保可靠的估计在车辆操作的全部范围,无论驾驶任务或系统中断.
- 在估计过程中实现不同车型之间的无过渡.
主要方法:
- 交叉组合'n'数量的过器用于状态估计.
- 使用基于决策树的机器学习算法来动态切换过器.
- 监测车辆横向运动的关键参数,以实现无模型转换.
主要成果:
- 证明了高精度,可靠和坚固的车辆侧滑角度估计.
- 在小和大侧滑角值中有效的性能.
- 在一个在雪覆盖的轨道上测试的原型车辆上成功验证.
结论:
- 拟议的波器组合技术与基于机器学习的切换机制显著改善了侧滑角度估计.
- 该方法在具有挑战性的现实驾驶场景中提供了稳健性和可靠性.
- 无模型转换可以提高整体估计性能.
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