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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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A Two-Layer Controller for Lateral Path Tracking Control of Autonomous Vehicles.

Zhiwei He1,2, Linzhen Nie1,2, Zhishuai Yin1,2

  • 1School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|July 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-layer controller for precise automated vehicle path tracking. The system ensures stability and accuracy across varying conditions by optimizing steering control and adapting to real-time dynamics.

Keywords:
autonomous vehiclelateral path tracking controlmodel predictive control

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Robotics

Background:

  • Highly automated vehicles require robust lateral path tracking for safety and performance.
  • Existing control systems face challenges with dynamic changes in vehicle velocity and road conditions.

Purpose of the Study:

  • To develop and validate a two-layer controller for accurate and stable lateral path tracking in automated vehicles.
  • To enhance robustness against variations in vehicle dynamics and road adhesion.

Main Methods:

  • Implemented a Linear Time-Varying Model Predictive Control (LTV-MPC) for the upper layer, optimizing prediction and control horizons using Particle Swarm Optimization (PSO).
  • Incorporated a slip angle constraint to prevent lateral force saturation and ensure vehicle stability.
  • Utilized a Radial Basis Function Neural Network Proportion-Integral-Derivative (RBFNN-PID) controller for the lower layer to adaptively adjust steering motor control signals based on real-time system identification.

Main Results:

  • The hierarchical controller demonstrated high path tracking accuracy in CarSim-Matlab/Simulink simulations.
  • Vehicle stability was maintained throughout the path tracking process under various conditions.
  • The controller exhibited robustness to dynamic changes in vehicle velocities and road adhesion coefficients.

Conclusions:

  • The proposed two-layer controller effectively achieves accurate and robust lateral path tracking for automated vehicles.
  • The integration of LTV-MPC and RBFNN-PID offers adaptive and stable control solutions.
  • The findings support the advancement of automated driving systems.