Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems

Simo Särkkä, Mauricio A. Alvarez, Neil D. Lawrence

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)

Abstract

This article is concerned with learning and stochastic control in physical systems which contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP) with certain parametrized covariance structures. The resulting latent force models (LFMs) can be seen as hybrid models that contain a first-principles physical model part and a non-parametric GP model part. We briefly review the statistical inference and learning methods for this kind of models, introduce stochastic control methodology for the models, and provide new theoretical observability and controllability results for them.

Original languageEnglish
Pages (from-to)2953-2960
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume64
Issue number7
DOIs
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Kalman filtering
  • Machine learning
  • Stochastic optimal control
  • Stochastic systems
  • System identification

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