Work

Robust Path-Tracking Control for Autonomous Vehicles: A Model-Reference-Adaptive-Control-Based Integrated Chassis Control Strategy

Year: 2025

Type: article

Abstract: Autonomous vehicles are often subjected to disturbances that compromise path-tracking accuracy and stability. Traditional chassis controllers that rely on fixed vehicle models exhibit performance limitations under such uncertainties. To address this challenge, we propose an adaptive integrated chassis control strategy that combines a linear quadratic regulator (LQR) and a model reference adaptive control (MRAC) framework. The LQR component generates nominal control commands, while the MRAC framework compensates in real time for model uncertainties and external disturbances. Simulation studies conducted in CarMaker and MATLAB/Simulink indicate that the proposed controller substantially improves path-tracking performance. Compared with conventional methods, the proposed controller reduces the root mean square error, peak error, and integral of the absolute error by up to 25.2%, 33.5%, and 34.6%, respectively. Overall, the proposed adaptive chassis controller shows enhanced vehicle robustness and stability in simulation under challenging driving conditions.

Source: Applied Sciences

DOI: 10.3390/app152312387

Authors: Siyeong Park, Taeyoung Oh, Jeesu Kim, Jinwoo Yoo

Institutions: Kookmin University, Pusan National University

Language: English

Topic: Vehicle Dynamics and Control Systems

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896