一种道路粘附系数-轮胎拐角刚度规范化方法,将分数顺序的多变量灰色模型与LSTM网络和车辆直接曲折时刻的强有力的控制相结合
Yufeng Lian1,2, Wenhuan Feng1, Shuaishi Liu1,2
1School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, Jilin, China.
Frontiers in neurorobotics
|August 25, 2023
概括
这项研究引入了一种新的方法,用于正常化车辆直接曲折时刻控制 (DYC) 系统的道路粘附和轮胎刚度. 该方法通过准确预测不同条件下的轮胎行为来提高车辆的稳定性和控制性能.
科学领域:
- 车辆动力学和控制控制
- 汽车工程 汽车工程
- 机器学习在汽车系统中的应用
背景情况:
- 有效的车辆直接曲面时刻控制 (DYC) 设计需要准确的道路附着系数和轮胎拐角刚度信息.
- 现有的方法在动态条件下可能缺乏稳定性或准确性.
- 这些参数的规范化对于可靠的控制器性能至关重要.
研究的目的:
- 为道路粘附系数和轮胎拐角刚度提出一种新的规范化方法.
- 将这种方法集成到车辆直接曲面时刻控制 (DYC) 系统的设计中.
- 通过模拟来验证拟议方法和DYC系统的有效性.
主要方法:
- 使用分数顺序多变量灰色模型 (FOMVGM) 来生成训练和测试数据集.
- 采用长期短期记忆 (LSTM) 网络,根据道路粘附系数预测轮胎拐角刚度.
- 开发了一种车辆横向动态模型,该模型包含了DYC强大的控制器设计的正常轮胎拐角刚度.
主要成果:
- FOMVGM和LSTM网络成功地预测了轮胎曲线硬度与道路粘附系数相关的情况.
- 规范化的参数使得能够构建一个强大的车辆横向动态模型.
- 模拟证明了标准化方法和DYC稳健控制系统在各种驾驶周期中的可行性和有效性.
结论:
- 拟议的规范化方法为车辆DYC系统设计提供了必要的信息.
- 整合FOMVGM和LSTM网络为预测关键车辆参数提供了一个强大的方法.
- 开发的DYC强大的控制系统显示了提高车辆稳定性和安全性的巨大潜力.
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