Smart Forgetting for Safe Online Learning with Gaussian Processes
Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:160-169, 2020.
The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles. While most control literature focuses on the analysis of a static dataset, online learning control, where data points are added while the controller is running, has rarely been studied in depth. In this paper, we present a novel approach for online learning control based on Gaussian process models. To avoid computational difficulties with growing datasets, we propose a safe forgetting mechanism. Using an entropy criterion, data points are evaluated with respect to the future trajectory of the closed loop system and are “forgotten” if the stability of the system can further be guaranteed. The approach is evaluated in a simulation and in a robotic experiment to show its real-time capability.