Improved Robust Model Predictive Trajectory Tracking Control for Intelligent Vehicles Based on Multi-Cell Hyperbody...

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.