  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.
