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Adaptive, Cautious, Predictive control with Gaussian Process Priors

2003

Conference Paper

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Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

Author(s): Murray-Smith, R. and Sbarbaro, D. and Rasmussen, CE. and Girard, A.
Journal: Proceedings of the 13th IFAC Symposium on System Identification
Pages: 1195-1200
Year: 2003
Month: August
Day: 0
Editors: Van den Hof, P., B. Wahlberg and S. Weiland

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Proceedings of the 13th IFAC Symposium on System Identification

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{2316,
  title = {Adaptive, Cautious, Predictive control with Gaussian Process Priors},
  author = {Murray-Smith, R. and Sbarbaro, D. and Rasmussen, CE. and Girard, A.},
  journal = {Proceedings of the 13th IFAC Symposium on System Identification},
  pages = {1195-1200},
  editors = {Van den Hof, P., B. Wahlberg and S. Weiland},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2003},
  month_numeric = {8}
}