Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems with Unknown and Changing Dynamics Performing Repetitive Tasks
This work addresses long-term, adaptive control for ground robots in repetitive tasks, offering incremental improvements in modeling and computational efficiency.
The paper tackles the problem of controlling a ground vehicle with unknown and changing dynamics during repetitive tasks by introducing a weighted Bayesian Linear Regression (wBLR) model, which improves accuracy and reduces computational cost in a Model Predictive Controller, demonstrated over 3.0 km of driving with physical and artificial changes.
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.