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A study on a vehicle semi-active suspension control system based on road elevation identification.

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This study introduces a novel algorithm using LIDAR for precise road elevation detection, enhancing semi-active suspension systems. This leads to improved vehicle ride comfort and handling stability through advanced road preview and adaptive shock absorption.

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Area of Science:

  • Automotive Engineering
  • Robotics and Control Systems
  • Sensor Technology

Background:

  • Semi-active suspension systems enhance vehicle dynamics but rely on accurate road information.
  • Existing methods for road detection may lack the precision needed for advanced suspension control.
  • Accurate perception of road elevation is crucial for optimizing vehicle ride comfort and stability.

Purpose of the Study:

  • To develop an advanced road elevation perception algorithm for vehicles.
  • To improve the performance of semi-active suspension systems through enhanced road sensing.
  • To integrate road preview capabilities into vehicle suspension control.

Main Methods:

  • A continuous multiple scanning recursive matching algorithm utilizing a single-line LIDAR sensor.
  • Recursive scanning and coordinate matching of historical and current echo signal data.
  • Regression of sensor height and pitch angle deviations for accurate pavement elevation estimation.
  • Development of a seven-degrees-of-freedom vehicle suspension model.
  • Implementation of a semi-active suspension controller using a diagonal recursive neural network trained by a genetic algorithm.
  • Proposal of a preview diagonal recursive neural network control strategy.

Main Results:

  • The proposed algorithm accurately estimates pavement elevation by compensating for sensor deviations.
  • Hardware-in-the-loop co-simulation demonstrated significant improvements in vehicle ride comfort.
  • Vehicle handling stability was enhanced due to the preview of upcoming road conditions.
  • The integrated control strategy effectively adjusted shock absorbers based on road elevation data.

Conclusions:

  • The developed LIDAR-based recursive matching algorithm provides accurate road elevation perception.
  • The preview diagonal recursive neural network control strategy effectively enhances semi-active suspension performance.
  • The study confirms the potential to significantly improve vehicle ride comfort and handling stability.