Learning-Based Model Predictive Control for Autonomous Racing

• Published in: IEEE robotics and automation letters Vol. 4; no. 4; pp. 3363 - 3370
• Main Authors: Kabzan, Juraj, Hewing, Lukas, Liniger, Alexander, Zeilinger, Melanie N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Automobiles; autonomous racing; Computational modeling; Data models; Data points; Distance learning; Gaussian process; Inflow; learning and adaptive systems; Machine learning; Mathematical model; Model learning for control; model predictive control; Predictive control; Predictive models; Race cars; Regression models; Vehicle dynamics
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2019.2926677

In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race car gotthard . One major issue in autonomous racing is that accurate vehicle models that cover the entire performance envelope of a race car are highly nonlinear, complex, and complicated to identify, rendering them impractical for control. To address this issue, we employ a relatively simple nominal vehicle model, which is improved based on measurement data and tools from machine learning.The resulting formulation is an online learning data-driven model predictive controller, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior. To improve the vehicle model online, we select from a constant in-flow of data points according to a criterion reflecting the information gain, and maintain a small dictionary of 300 data points. The framework is tested on the full-size AMZ Driverless race car, where it is able to improve the vehicle model and reduce lap times by <inline-formula><tex-math notation="LaTeX"> {\mathbf{10}{\%}}</tex-math></inline-formula> while maintaining safety of the vehicle.