TR2021-026

Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels


    •  Berntorp, K., "Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels", Automatica, DOI: https:/​/​doi.org/​10.1016/​j.automatica.2021.109613, Vol. 129, March 2021.
      BibTeX TR2021-026 PDF
      • @article{Berntorp2021mar,
      • author = {Berntorp, Karl},
      • title = {Online Bayesian Inference and Learning of Gaussian-Process State-SpaceModels},
      • journal = {Automatica},
      • year = 2021,
      • volume = 129,
      • month = mar,
      • doi = {https://doi.org/10.1016/j.automatica.2021.109613},
      • issn = {0005-1098},
      • url = {https://www.merl.com/publications/TR2021-026}
      • }
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  • Research Areas:

    Control, Signal Processing

Abstract:

This paper addresses the recursive joint inference (state estimation) and learning (system identification) problem for nonlinear systems admitting a state-space formulation. We model the system as a Gaussian-process state-space model (GP-SSM) and leverage a recently developed reduced-rank formulation of GP-SSMs to enable efficient, online learning. The unknown dynamical system is expressed as a basis-function expansion, where a connection to the GP makes it possible to systematically assign priors to the basis-function weights. The approach is formulated within the sequential Monte-Carlo framework, wherein each particle retains its own weights of the basis functions, which are updated recursively as measurements arrive. We report competitive results when compared to a state-of-the art offline Bayesian learning method. We also apply the method to a case study concerning tire-friction estimation. The results indicate that our method can accurately learn the tire friction using automotive-grade sensors in an online setting, and quickly detect sudden changes of the road surface.