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

TL;DR

This paper introduces a predictive control method using weighted Bayesian Linear Regression for long-term, safe path following in systems with unknown and evolving dynamics, demonstrated on a ground robot.

Contribution

The paper presents a novel control framework combining weighted Bayesian Linear Regression with Tube MPC for adaptive, efficient, and safe long-term operation of systems with changing dynamics.

Findings

wBLR outperforms Gaussian Process Regression in accuracy and generalization.

The approach enables quick adaptation and long-term learning in a unified framework.

Experimental results show successful long-distance driving with dynamic changes.

Abstract

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.