  We present a control method for improved repetitive path following for a
ground vehicle that is geared towards long-term operation where the operating
conditions can change over time and are initially unknown. We use weighted
Bayesian Linear Regression (wBLR) to model the unknown dynamics, and show how
this simple model is more accurate in both its estimate of the mean behaviour
and model uncertainty than Gaussian Process Regression and generalizes to novel
operating conditions with little or no tuning. In addition, wBLR allows us to
use fast adaptation and long-term learning in one, unified framework, to adapt
quickly to new operating conditions and learn repetitive model errors over
time. This comes with the added benefit of lower computational cost, longer
look-ahead, and easier optimization when the model is used in a stochastic
Model Predictive Controller (MPC). In order to fully capitalize on the long
prediction horizons that are possible with this new approach, we use Tube MPC
to reduce the growth of predicted uncertainty. We demonstrate the effectiveness
of our approach in experiment on a 900\,kg ground robot showing results over
3.0\,km of driving with both physical and artificial changes to the robot's
dynamics. All of our experiments are conducted using a stereo camera for
localization.
