How Training Data Impacts Performance in Learning-Based Control

Armin Lederer, Alexandre Capone, Jonas Umlauft, Sandra Hirche

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


When first principle models cannot be derived due to the complexity of the real system, data-driven methods allow us to build models from system observations. As these models are employed in learning-based control, the quality of the data plays a crucial role for the performance of the resulting control law. Nevertheless, there hardly exist measures for assessing training data sets, and the impact of the spatial distribution of the data on the closed-loop system properties is largely unknown. This letter derives - based on Gaussian process models - an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as ρ-gap, and derive the ultimate bound for the tracking error under consideration of the model uncertainty. We show how the ρ-gap can be applied to a feedback linearizing control law and provide numerical illustrations for our approach.

Original languageEnglish
Article number9131821
Pages (from-to)905-910
Number of pages6
JournalIEEE Control Systems Letters
Issue number3
StatePublished - Jul 2021


  • Lyapunov methods
  • Machine learning
  • information theory and control
  • nonlinear systems identification
  • uncertain systems


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