Adaptive Articulation Angle Preview-Based Path-Following Algorithm for Tractor-Semitrailer Using Optimal Control

Area of Science:

• Robotics
• Control Systems
• Automotive Engineering

Background:

• Existing path-following algorithms (PFAs) are primarily designed for single-unit vehicles (SUVs).
• PFAs often neglect trailer dynamics in articulated vehicles (AVs), leading to tracking errors and stability issues during turns.

Purpose of the Study:

• To develop an Adaptive Articulation Angle Preview-based Path-Following Algorithm (AAAP-PFA) specifically for articulated vehicles (AVs).
• To enhance trajectory tracking and lateral stability performance in AVs during path following.

Main Methods:

• Utilized a linear vehicle dynamics model as the prediction model.
• Incorporated an offset distance derived from the articulation angle into the preview distance.
• Implemented an adaptive posture control strategy for balancing tracking and stability.
• Introduced a feedback correction method to address prediction mismatches and improve steering robustness.

Main Results:

• The AAAP-PFA demonstrated superior adaptability in managing the trade-off between trajectory tracking and lateral stability.
• Simulation results showed the novel algorithm significantly improved steering control robustness compared to SUV-PFAs.
• The algorithm effectively addressed prediction mismatches inherent in using a linear dynamics model.

Conclusions:

• The AAAP-PFA offers a robust solution for path following in articulated vehicles.
• It effectively enhances both tracking accuracy and ride stability, crucial for AVs in complex maneuvers.
• The adaptive control and feedback correction strategies contribute to its improved performance and robustness.