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相关概念视频

Response Surface Methodology01:16

Response Surface Methodology

91
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
91
Rapidly Varying Flow01:24

Rapidly Varying Flow

51
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
51
Major Losses in Pipes01:28

Major Losses in Pipes

695
When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to...
695

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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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
PubMed
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.

关键词:
适应式卡尔曼过器非线性自回归与外源性自回归非线性悬挂是一种非线性悬挂.路面粗度识别 道路粗度识别平方根立方形卡尔曼波器

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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.