Parameter Filter-based Event-triggered Learning

European Control Conference (ECC), pp. 3021–3026

Abstract

Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption—access to an accurate model of the system. In practice, models are neither perfect nor time-invariant. Even precisely tuned estimates of unknown parameters will deteriorate over time. We propose to combine statistical tests with dedicated parameter filters that track unknown system parameters from state data. These filters yield point estimates of the unknown parameters and, further, an inherent notion of uncertainty. This allows us to detect changes in the dynamics. When the point estimate leaves the confidence region, we trigger active learning experiments. Thus, models are only updated when necessary and statistically significant while ensuring guaranteed improvement, which we call event-triggered learning. We validate the proposed method in numerical simulations of a DC motor in combination with model predictive control.

Authors 3

1. Sebastian Schlor

   University of Stuttgart

   Affiliation as printed

   University of Stuttgart, Institute for Systems Theory and Automatic Control,Stuttgart,Germany,70550

2. Friedrich Solowjow Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Aachen,Germany,52068

3. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering,Aachen,Germany,52068