Särkkä, S., Álvarez, M.A. and Lawrence, N.D. orcid.org/0000-0001-9258-1030 (2018) Gaussian process latent force models for learning and stochastic control of physical systems. IEEE Transactions on Automatic Control. ISSN 0018-9286
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.
Metadata
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. | ||||||
Keywords: | Machine learning; Stochastic optimal control; Stochastic systems; System identification; Kalman filtering | ||||||
Dates: |
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Institution: | The University of Sheffield | ||||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) | ||||||
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Depositing User: | Symplectic Sheffield | ||||||
Date Deposited: | 09 Oct 2018 14:57 | ||||||
Last Modified: | 10 Oct 2018 14:10 | ||||||
Published Version: | https://doi.org/10.1109/TAC.2018.2874749 | ||||||
Status: | Published online | ||||||
Publisher: | IEEE | ||||||
Refereed: | Yes | ||||||
Identification Number: | https://doi.org/10.1109/TAC.2018.2874749 |