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Predictive Path-Tracking Control of an Autonomous Electric Vehicle with Various Multi-Actuation Topologies.

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Area of Science:

  • Robotics and Control Systems
  • Autonomous Vehicle Dynamics
  • Electric Vehicle Technology

Background:

  • Autonomous electric vehicles require advanced control for precise path tracking.
  • Over-actuation, using capabilities like four-wheel steering (4WS) and torque vectoring (TV), offers potential for enhanced vehicle dynamics.
  • Existing control strategies may not fully address nonlinearities and actuator constraints at the limits of handling.

Purpose of the Study:

  • To develop and evaluate path-tracking control strategies for an over-actuated autonomous electric vehicle.
  • To investigate the performance benefits of combined 4WS and TV actuation.
  • To assess the real-time feasibility of advanced control formulations considering vehicle nonlinearities and constraints.

Main Methods:

  • Development of a nonlinear model predictive controller (NMPC) accounting for vehicle nonlinearities and actuator constraints.
  • Implementation and comparison of controllers with varying actuation formulations (e.g., 4WS, TV, combined).
  • High-fidelity simulation environment for testing under handling limit scenarios and real-time validation on a target machine.

Main Results:

  • The combined 4WS and TV actuation strategy yielded the best path-tracking performance, particularly under handling limit conditions.
  • The proposed over-actuation control strategy demonstrated compatibility with different actuation levels and real-time execution.
  • Simulation results indicated that sampling time and prediction horizon influence control performance and computational load, allowing for a performance-computation trade-off.

Conclusions:

  • Over-actuation topology significantly enhances path-tracking performance in autonomous electric vehicles, especially near handling limits.
  • The developed nonlinear model predictive controller effectively manages nonlinear vehicle dynamics and actuator constraints for real-time operation.
  • The study confirms the practical applicability of advanced over-actuation control for robust autonomous vehicle navigation.