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Train Trajectory-Following Control Method Using Virtual Sensors.

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  • 1Rail Transit Institute, Tongji University, Shanghai 200333, China.

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This study presents a new control strategy for articulated virtual rail trains, enabling precise trajectory following for the rear vehicle. The method ensures stable performance across various conditions, enhancing transportation system efficiency.

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articulated vehiclelateral controlnonlinear dynamicstrajectory following

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Area of Science:

  • Robotics and Control Systems
  • Mechanical Engineering
  • Transportation Systems

Background:

  • Trajectory-following control is crucial for articulated virtual rail train systems.
  • Existing methods may lack robustness or real-time adaptability for complex dynamics.

Purpose of the Study:

  • To develop and validate a robust trajectory-following control strategy for articulated virtual rail trains.
  • To enhance the practical applicability of virtual rail train transportation systems.

Main Methods:

  • Derivation of a planar nonlinear dynamics model using the Euler-Lagrange method.
  • Design of a feedback linearization control algorithm based on the vehicle dynamics model.
  • Real-time calculation of desired vehicle states using a vector analysis method and virtual sensors.

Main Results:

  • Successful trajectory following for the rear vehicle demonstrated in simulations.
  • Good vehicle dynamics performance maintained during lane changes and circular curves.
  • The control algorithm showed robustness to variations in vehicle mass, speed, tire stiffness, and road friction.

Conclusions:

  • The proposed control strategy effectively achieves trajectory following for articulated virtual rail trains.
  • The system exhibits robust performance and good dynamics, suitable for practical applications.
  • This research contributes to the advancement of autonomous transportation systems.