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

Simo Särkkä; Mauricio A. Álvarez; Neil D. Lawrence

doi:10.1109/TAC.2018.2874749

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

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

IEEE Transactions on Automatic Control, IEEE 64(7):2953-2960, 2018.

Abstract

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

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Cite this Paper

BibTeX


@Article{Sarkka-lfmcontrol18,
  title = 	 {Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems},
  author = 	 {Särkkä, Simo and Álvarez, Mauricio A. and Lawrence, Neil D.},
  journal =      {IEEE Transactions on Automatic Control},
  pages = 	 {2953--2960},
  year = 	 {2018},
  volume = 	 {64},
  number =       {7},
  publisher =    {IEEE},
  doi = 	 {10.1109/TAC.2018.2874749},
  url = 	 {/publications/gaussian-process-latent-force-models-for-learning-and-stochastic-control-of-physical-systems.html},
  abstract = 	 {This paper is concerned with learning and stochastic control in
physical systems that contain unknown input signals. These unknown
signals are modeled as Gaussian processes (GP) with certain
parameterized covariance structures. The resulting latent force
models can be seen as hybrid models that contain a first-principle
physical model part and a nonparametric GP model part. We briefly
review the statistical inference and learning methods for this kind
of models, introduce stochastic control methodology for these
models, and provide new theoretical observability and
controllability results for them.
}
}

Endnote

%0 Journal Article
%T Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
%A Simo Särkkä
%A Mauricio A. Álvarez
%A Neil D. Lawrence
%J IEEE Transactions on Automatic Control
%D 2018	
%F Sarkka-lfmcontrol18
%I IEEE
%P 2953--2960
%R 10.1109/TAC.2018.2874749
%U /publications/gaussian-process-latent-force-models-for-learning-and-stochastic-control-of-physical-systems.html
%V 64
%N 7
%X This paper is concerned with learning and stochastic control in
physical systems that contain unknown input signals. These unknown
signals are modeled as Gaussian processes (GP) with certain
parameterized covariance structures. The resulting latent force
models can be seen as hybrid models that contain a first-principle
physical model part and a nonparametric GP model part. We briefly
review the statistical inference and learning methods for this kind
of models, introduce stochastic control methodology for these
models, and provide new theoretical observability and
controllability results for them.

RIS


TY  - JOUR
TI  - Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
AU  - Simo Särkkä
AU  - Mauricio A. Álvarez
AU  - Neil D. Lawrence
DA  - 2018/10/08	
ID  - Sarkka-lfmcontrol18
PB  - IEEE
VL  - 64
IS  - 7
SP  - 2953
EP  - 2960
DO  - 10.1109/TAC.2018.2874749
UR  - /publications/gaussian-process-latent-force-models-for-learning-and-stochastic-control-of-physical-systems.html
AB  - This paper is concerned with learning and stochastic control in
physical systems that contain unknown input signals. These unknown
signals are modeled as Gaussian processes (GP) with certain
parameterized covariance structures. The resulting latent force
models can be seen as hybrid models that contain a first-principle
physical model part and a nonparametric GP model part. We briefly
review the statistical inference and learning methods for this kind
of models, introduce stochastic control methodology for these
models, and provide new theoretical observability and
controllability results for them.

ER  -

APA


Särkkä, S., Álvarez, M.A. & Lawrence, N.D.. (2018). Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. IEEE Transactions on Automatic Control 64(7):2953-2960 doi:10.1109/TAC.2018.2874749 Available from /publications/gaussian-process-latent-force-models-for-learning-and-stochastic-control-of-physical-systems.html.