一种基于NARX-PASCKF的非线性悬挂道路粗度识别方法
Jiahao Qian1, Yinong Li1,2, Ling Zheng1,2
1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究引入了一种混合算法,用于准确识别车辆的道路粗度,从而提高安全性. 该方法结合了非线性自回归与外源输入 (NARX) 和自适应卡尔曼过以提高性能.
科学领域:
- 汽车工程 汽车工程
- 控制系统 控制系统
- 信号处理 信号处理
背景情况:
- 道路粗程度显著影响车辆的安全性和动态性.
- 车辆悬架的非线性特性使得准确的道路粗度估计变得复杂.
- 现有的方法与模型不确定性和融合问题作斗争.
研究的目的:
- 开发一种混合算法,用于在非线性车辆悬架系统中准确识别道路粗度.
- 提高道路粗度估计器的准确性和趋同率.
- 解决估计算法中的非融合问题.
主要方法:
- 一个混合算法集成非线性自动回归与外源输入 (NARX) 和一个过程噪声自适应的平方根立方卡尔曼波器 (PASCKF).
- 使用车辆加速数据来驱动基于NARX的道路粗度识别系统.
- 将NARX估计的道路粗度转换为PASCKF的工艺噪声共变量.
- 实施切换策略以优化 PASCKF 的性能和减轻非融合.
主要成果:
- 拟议的混合算法与独立算法相比,显示出更高的识别准确性.
- 提高了道路粗度估计的融合率和更好的适应性.
- 通过模拟数据和实际车辆实验验证.
结论:
- 混合NARX-PASCKF算法有效地识别非线性悬挂系统中的道路粗.
- 这种方法在准确性和适应性方面比传统方法提供了显著的改进.
- 这些发现有助于提高车辆安全性和动态响应分析.
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