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Related Experiment Video

Updated: Jun 7, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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A Nonlinear Suspension Road Roughness Recognition Method Based on NARX-PASCKF.

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
Summary
This summary is machine-generated.

This study introduces a hybrid algorithm for accurate road roughness identification in vehicles, enhancing safety. The method combines nonlinear auto-regressive with exogenous inputs (NARX) and adaptive Kalman filtering for improved performance.

Keywords:
adaptive Kalman filteringnonlinear auto-regressive with exogenousnonlinear suspensionroad roughness identificationsquare root cubature Kalman filter

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Signal Processing

Background:

  • Road roughness significantly affects vehicle safety and dynamics.
  • Nonlinear characteristics of vehicle suspensions complicate accurate road roughness estimation.
  • Existing methods struggle with model uncertainties and convergence issues.

Purpose of the Study:

  • To develop a hybrid algorithm for accurate road roughness identification in nonlinear vehicle suspension systems.
  • To improve the accuracy and convergence rate of road roughness estimators.
  • To address non-convergence issues in estimation algorithms.

Main Methods:

  • A hybrid algorithm integrating Nonlinear Auto-Regressive with Exogenous inputs (NARX) and a Process Noise adaptive Square Root Cubature Kalman Filter (PASCKF).
  • Utilizing vehicle acceleration data to drive an NARX-based road roughness identification system.
  • Converting NARX-estimated road roughness into process noise covariance for the PASCKF.
  • Implementing a switching strategy to optimize PASCKF performance and mitigate non-convergence.

Main Results:

  • The proposed hybrid algorithm demonstrates superior identification accuracy compared to standalone algorithms.
  • Enhanced convergence rate and improved adaptability in road roughness estimation.
  • Validation through both simulation data and actual vehicle experiments.

Conclusions:

  • The hybrid NARX-PASCKF algorithm effectively identifies road roughness in nonlinear suspension systems.
  • This approach offers significant improvements in accuracy and adaptability over traditional methods.
  • The findings contribute to enhanced vehicle safety and dynamic response analysis.