  Delays endanger safety of autonomous systems operating in a rapidly changing
environment, such as nondeterministic surrounding traffic participants in
autonomous driving and high-speed racing. Unfortunately, delays are typically
not considered during the conventional controller design or learning-enabled
controller training phases prior to deployment in the physical world. In this
paper, the computation delay from nonlinear optimization for motion planning
and control, as well as other unavoidable delays caused by actuators, are
addressed systematically and unifiedly. To deal with all these delays, in our
framework: 1) we propose a new filtering approach with no prior knowledge of
dynamics and disturbance distribution to adaptively and safely estimate the
time-variant computation delay; 2) we model actuation dynamics for steering
delay; 3) all the constrained optimization is realized in a robust tube model
predictive controller. For the application merits, we demonstrate that our
approach is suitable for both autonomous driving and autonomous racing. Our
approach is a novel design for a standalone delay compensation controller. In
addition, in the case that a learning-enabled controller assuming no delay
works as a primary controller, our approach serves as the primary controller's
safety guard.

Robotics