Model-Predictive-Control-Based Path Tracking Controller of Autonomous Vehicle Considering Parametric Uncertainties and Velocity-Varying

The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach i...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2021-09, Vol.68 (9), p.8698-8707
Main Authors: Cheng, Shuo, Li, Liang, Chen, Xiang, Wu, Jian, Wang, Hong-da
Format: Article
Language:English
Subjects:
Apexes
Automated steering system
Automatic control
Automation
Autonomous vehicles
Axles
Control systems design
Controllers
Linear matrix inequalities
linear matrix inequalities (LMI)
Mathematical model
Mathematical models
Parameter robustness
Parameter uncertainty
parametric uncertainties
Path tracking
Predictive control
Robust control
robust model predictive control (MPC)
Robustness
Steering
Tires
Tracking control
Traffic speed
Uncertainty
Vehicle dynamics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach is proposed in this article. First, considering tire nonlinear characteristics, the state and control matrices are modified, then the time-varying vehicle speed is considered and a linear parameter varying lateral model is established through utilizing a polytope with finite vertices to describe vehicle longitudinal velocity. Then, the MPC-based vehicle path tracking controller, which is robust against parameter uncertainties, is designed; the proposed controller can be solved via a set of linear matrix inequalities (LMI), which are derived from Lyapunov asymptotic stability and the minimization of the worst case infinite horizon quadratic objective function. The proposed control system is evaluated by both cosimulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed controller, and it can ensure the control accuracy and strong robustness.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2020.3009585