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We present a control method for improved repetitive path following for aground vehicle that is geared towards long-term operation where the operatingconditions can change over time and are initially unknown. We use weightedBayesian Linear Regression (wBLR) to model the unknown dynamics, and show howthis simple model is more accurate in both its estimate of the mean behaviourand model uncertainty than Gaussian Process Regression and generalizes to noveloperating conditions with little or no tuning. In addition, wBLR allows us touse fast adaptation and long-term learning in one, unified framework, to adaptquickly to new operating conditions and learn repetitive model errors overtime. This comes with the added benefit of lower computational cost, longerlook-ahead, and easier optimization when the model is used in a stochasticModel Predictive Controller (MPC). In order to fully capitalize on the longprediction horizons that are possible with this new approach, we use Tube MPCto reduce the growth of predicted uncertainty. We demonstrate the effectivenessof our approach in experiment on a 900\,kg ground robot showing results over3.0\,km of driving with both physical and artificial changes to the robot'sdynamics. All of our experiments are conducted using a stereo camera forlocalization.

Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems with Unknown and Changing Dynamics Performing Repetitive Tasks