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Event-triggered human-machine shared steering online learning control strategy to accommodate driver behavioral

Wanqing Shi1, Hongyan Guo1, Jun Liu2

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Summary

This study introduces an event-triggered shared control strategy for vehicles, adapting in real-time to driver behavior uncertainties. The approach ensures system stability and precise steering control with reduced communication needs.

Keywords:
Autonomous vehicleDriver behavior uncertaintyEvent-triggered strategyGaussian processHuman-machine shared control

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Area of Science:

  • Robotics and Autonomous Systems
  • Control Theory
  • Human-Machine Interaction

Background:

  • Vehicle control systems face challenges from stochastic driver behavior and uncertain dynamics.
  • Existing models often require precise system identification, limiting adaptability.
  • Real-time adaptation is crucial for safe and efficient shared control.

Purpose of the Study:

  • To propose an event-triggered shared control strategy for adaptive vehicle control.
  • To address uncertainties in driver behavior and system dynamics.
  • To ensure system stability and enhance steering precision.

Main Methods:

  • Modeling the vehicle as a nonlinear time-varying (NTV) system.
  • Employing sparse Gaussian process regression (GPR) for online system identification.
  • Designing an adaptive feedback linearization (AFL) controller with stability proven via common Lyapunov function (CLF).
  • Implementing an event-triggered mechanism based on GPR uncertainty thresholds.

Main Results:

  • Demonstrated robustness and adaptability to diverse driver behaviors through simulations and driver-in-the-loop experiments.
  • Achieved precise steering control under uncertain conditions.
  • Significantly reduced communication overhead compared to traditional methods.

Conclusions:

  • The proposed event-triggered shared control strategy effectively manages driver behavior stochasticity and system uncertainty.
  • The adaptive GPR and AFL controller ensure system stability and performance.
  • This approach offers a practical solution for enhancing autonomous and semi-autonomous vehicle control.