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Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier

Ding Dong1, Hongtao Ye1,2,3, Wenguang Luo1,3

  • 1School of Automation, Guangxi University of Science and Technology, Liuzhou 545036, China.

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|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances autonomous vehicle trajectory tracking using the alternating direction multiplier method (ADMM) with model predictive control (MPC). The ADMM approach significantly improves computational speed and tracking accuracy for real-time performance.

Keywords:
alternately direction multiplier method (ADMM)model predictive control (MPC)quadratic programmingreal-time performancetrajectory tracking

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Area of Science:

  • Robotics and Control Systems
  • Automotive Engineering
  • Computational Optimization

Background:

  • Real-time trajectory tracking is crucial for autonomous vehicle safety and efficiency.
  • Model Predictive Control (MPC) is a common control strategy, but its computational demands can limit real-time performance.
  • Existing methods like Active Set Method (ASM) and Interior Point Method (IPM) face challenges with computational speed.

Purpose of the Study:

  • To improve the real-time performance of autonomous vehicle trajectory tracking.
  • To enhance the computational speed of Model Predictive Control (MPC) algorithms.
  • To evaluate the effectiveness of the Alternating Direction Multiplier Method (ADMM) in optimizing MPC for vehicle control.

Main Methods:

  • Applied the Alternating Direction Multiplier Method (ADMM) to the receding optimization of Model Predictive Control (MPC).
  • Formulated the vehicle dynamics model and output equation, introducing auxiliary and dual variables.
  • Transformed the quadratic programming problem and vehicle dynamics constraints into an ADMM- solvable form, utilizing a decreasing penalty factor.

Main Results:

  • The proposed ADMM-based MPC algorithm demonstrated improved trajectory tracking accuracy.
  • The method achieved superior real-time performance compared to Active Set Method (ASM) and Interior Point Method (IPM).
  • Computational time remained stable even with increased prediction time domains, confirming ADMM's effectiveness.

Conclusions:

  • The ADMM approach effectively enhances the real-time computational speed of MPC for autonomous vehicle trajectory tracking.
  • The proposed method offers a viable solution for achieving both high accuracy and real-time performance in autonomous driving systems.
  • ADMM integration presents a significant advancement in optimizing control algorithms for dynamic vehicle applications.