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A Tube Model Predictive Control Method for Autonomous Lateral Vehicle Control Based on Sliding Mode Control.

Yong Dai^1,2, Duo Wang¹

¹School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This study introduces a new control algorithm for autonomous vehicles (AVs) that improves lateral path tracking on challenging roads. The combined robust sliding mode control (SMC) and tube model predictive control (MPC) enhances safety and accuracy against disturbances.

Keywords:

autonomous vehicle (AV)model predictive control (MPC)path tracking sliding mode control (SMC)tube MPC

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Area of Science:

Robotics
Control Systems Engineering
Automotive Engineering

Background:

Autonomous vehicle (AV) lateral path tracking is crucial for safety and efficiency.
Real-world driving conditions like slippery roads introduce external disturbances and unmodeled uncertainties.
Conventional control methods often fail to adequately address these challenges.

Purpose of the Study:

To enhance the lateral path tracking control of autonomous vehicles (AVs) under external disturbances.
To develop a robust control algorithm that overcomes limitations of existing methods.

Main Methods:

A novel algorithm combining robust sliding mode control (SMC) and tube model predictive control (MPC) is proposed.
MPC is used for nominal system trajectory tracking, while SMC ensures robustness against uncertainties.
An auxiliary tube SMC control law is derived to maintain system performance.

Main Results:

The proposed hybrid SMC-MPC algorithm demonstrates superior robustness and tracking accuracy compared to conventional tube MPC, LQR, and MPC.
Experimental results validate the effectiveness of the algorithm in handling unmodeled uncertainties and external disturbances.

Conclusions:

The proposed robust SMC and tube MPC combination effectively enhances lateral path tracking for AVs.
This approach offers improved safety and reliability in complex driving scenarios.