Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
IEEE Transactions on Automatic Control
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Sarkka, S., Alvarez, M., & Lawrence, N. (2019). Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems. IEEE Transactions on Automatic Control, 64 (7)https://doi.org/10.1109/TAC.2018.2874749
© 1963-2012 IEEE. 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.
External DOI: https://doi.org/10.1109/TAC.2018.2874749
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301305
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