Can Learning Deteriorate Control? Analyzing Computational Delays in
  Gaussian Process-Based Event-Triggered Online Learning
Xiaobing Dai Armin Lederer Zewen Yang Sandra Hirche
When the dynamics of systems are unknown, supervised machine learning
techniques are commonly employed to infer models from data. Gaussian process
(GP) regression is a particularly popular learning method for this purpose due
to the existence of prediction error bounds. Moreover, GP models can be
efficiently updated online, such that event-triggered online learning
strategies can be pursued to ensure specified tracking accuracies. However,
existing trigger conditions must be able to be evaluated at arbitrary times,
which cannot be achieved in practice due to non-negligible computation times.
Therefore, we first derive a delay-aware tracking error bound, which reveals an
accuracy-delay trade-off. Based on this result, we propose a novel event
trigger for GP-based online learning with computational delays, which we show
to offer advantages over offline trained GP models for sufficiently small
computation times. Finally, we demonstrate the effectiveness of the proposed
event trigger for online learning in simulations.