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This study introduces an improved robust model predictive control (RMPC) for vehicle trajectory tracking. The RMPC method enhances control accuracy and robustness against model parameter uncertainties, improving safety.

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Area of Science:

  • Automotive Engineering
  • Control Systems Theory
  • Robotics

Background:

  • Vehicle trajectory tracking is crucial for autonomous driving.
  • Model parameter uncertainties pose significant challenges to control system performance.
  • Traditional Model Predictive Control (MPC) can be sensitive to these perturbations.

Purpose of the Study:

  • To propose an improved robust model predictive control (RMPC) method.
  • To address the problem of model parameter perturbation in vehicle trajectory tracking control.
  • To enhance the robustness and real-time performance of vehicle control systems.

Main Methods:

  • Utilized a two-degree-of-freedom vehicle model and Serret Frenet error model.
  • Employed multi-cell hypercube vertex modeling to represent parameter uncertainties.
  • Implemented dual-layer optimization with finite time domain optimization and terminal constraints.
  • Integrated Lyapunov theory to design a control invariant set.

Main Results:

  • Reduced peak lateral deviation from 1.0 m to 0.2 m.
  • Converged heading deviation to within 2 degrees.
  • Significantly decreased mean and root mean square control errors compared to traditional MPC.
  • Demonstrated good robustness and real-time performance under parameter perturbations.

Conclusions:

  • The proposed RMPC method effectively handles vehicle model parameter perturbations.
  • RMPC offers superior performance in complex road conditions and vehicle state transitions.
  • This approach enhances the reliability and safety of vehicle trajectory tracking control.