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Updated: Oct 13, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control.

Zhejun Huang^1,2,3, Huiyun Li^1,2,3, Wenfei Li^1,2,3

¹Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This study introduces a model predictive control (MPC) method for autonomous vehicle trajectory tracking. Using backward Euler integration improves tracking accuracy significantly with minimal computational cost.

Keywords:

autonomous driving model predictive control real-time control trajectory tracking

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Movement Retraining using Real-time Feedback of Performance

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Published on: January 17, 2013

Area of Science:

Autonomous Systems
Control Theory
Robotics

Background:

Precise trajectory tracking is crucial for autonomous vehicle control.
Existing methods often face limitations in real-time performance and accuracy.

Purpose of the Study:

To develop an improved trajectory-tracking method for autonomous vehicles.
To enhance the accuracy and real-time capabilities of model predictive control (MPC).

Main Methods:

Proposed a trajectory-tracking method based on model predictive control (MPC).
Utilized backward Euler integration for predictive model establishment, differing from the forward Euler method.
Implemented constraints on the control law and employed a warm-start technique for real-time performance.

Main Results:

The MPC-based controller demonstrated improved stability and tracking performance.
Significant reductions in maximum lateral errors were observed (69.09%, 47.89%, 78.66%).
The controller achieved real-time performance with calculation times below 0.0203 s, fitting within the control period.

Conclusions:

The proposed backward Euler-based MPC method offers superior trajectory-tracking performance for autonomous vehicles.
The approach effectively balances accuracy improvements with real-time computational demands.
This method represents a viable advancement for precise autonomous vehicle control systems.