Path Tracking of Highly Dynamic Autonomous Vehicle Trajectories via Iterative Learning Control

Iterative learning control has been successfully used for several decades to improve the performance of control systems that perform a single repeated task. Using information from prior control executions, learning controllers gradually determine open-loop control inputs whose reference tracking performance can exceed that of traditional feedback-feedforward control algorithms. This paper considers iterative learning control for a previously unexplored field: autonomous racing. Racecars are driven multiple laps around the same sequence of turns while operating near the physical limits of tire-road friction, where steering dynamics become highly nonlinear and transient, making accurate path tracking difficult. However, because the vehicle trajectory is identical for each lap in the case of single-car racing, the nonlinear vehicle dynamics and unmodelled road conditions are repeatable and can be accounted for using iterative learning control, provided the tire force limits have not been exceeded. This paper describes the design and application of proportional-derivative (PD) and quadratically optimal (Q-ILC) learning algorithms for multiple-lap path tracking of an autonomous race vehicle. Simulation results are used to tune controller gains and test convergence, and experimental results are presented on an Audi TTS race vehicle driving several laps around Thunderhill Raceway in Willows, CA at lateral accelerations of up to 8 $\mathrm{m/s^2}$. Both control algorithms are able to correct transient path tracking errors and improve the performance provided by a reference feedforward controller.