Robust Path-Tracking Control for Autonomous Vehicles: A Model-Reference-Adaptive-Control-Based Integrated Chassis Control Strategy
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Vehicle Dynamics and Control Systems · Control and Dynamics of Mobile Robots · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea
Department of Cogno-Mechatronics Engineering, Pusan National University, Busan 46241, Republic of Korea
Department of Automobile and IT Convergence, Kookmin University, Seoul 02707, Republic of Korea
Funding
National Research Foundation of Korea
References (49)
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