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Observer-based backstepping control method using reduced lateral dynamics for autonomous lane-keeping system.

Chang Mook Kang1, Wonhee Kim2, Chung Choo Chung3

  • 1Agency for Defense Development, Daejeon, Republic of Korea.

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|October 8, 2018
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Summary
This summary is machine-generated.

This study introduces an advanced backstepping control with an augmented observer for autonomous lane-keeping systems. The method enhances lateral control and disturbance compensation for safer driving.

Keywords:
Augmented observerBacksteppingLateral controlStabilityVehicle model

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Automotive Engineering

Background:

  • Autonomous lane-keeping systems require robust control strategies to handle uncertainties.
  • Existing models may not adequately capture complex lateral dynamics or external disturbances.

Purpose of the Study:

  • To develop a novel backstepping control method with an augmented observer for autonomous lane-keeping.
  • To introduce a reduced second-order model for lateral vehicle motion that incorporates look-ahead distance.
  • To address unknown system parameters and external disturbances effectively.

Main Methods:

  • Derivation of a novel reduced second-order model for lateral vehicle dynamics.
  • Design of an augmented observer to estimate system states and lumped disturbances.
  • Development of a backstepping control strategy for enhanced lateral control and disturbance compensation.
  • Stability analysis using the input-to-state stable property.

Main Results:

  • The reduced model simplifies lateral vehicle motion control to linear second-order dynamics.
  • The augmented observer successfully estimates full states and lumped disturbances.
  • The backstepping controller improves lateral control accuracy and disturbance compensation.
  • Input-to-state stability of the closed-loop system is proven.

Conclusions:

  • The proposed backstepping control with an augmented observer offers a robust solution for autonomous lane-keeping.
  • The novel reduced model provides an effective representation of lateral vehicle dynamics.
  • Validated through CarSim and MATLAB/Simulink simulations, the method demonstrates strong performance.