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  Adaptive, Cautious, Predictive control with Gaussian Process Priors

Murray-Smith, R., Sbarbaro, D., Rasmussen, C., & Girard, A. (2003). Adaptive, Cautious, Predictive control with Gaussian Process Priors. IFAC Proceedings Volumes, 36(16), 1155-1160.

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 Creators:
Murray-Smith, R, Author
Sbarbaro, D, Author
Rasmussen, CE1, 2, Author           
Girard, A, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: 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.

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 Dates: 2003-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2316
DOI: 10.1016/S1474-6670(17)34915-7
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Title: 13th IFAC Symposium on System Identification (SYSID 2003)
Place of Event: Rotterdam, The Netherlands
Start-/End Date: 2003-08-27 - 2003-08-29

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Title: IFAC Proceedings Volumes
Source Genre: Journal
 Creator(s):
Van den Hof, P, Editor
Wahlberg, P, Editor
Weiland, S, Editor
Affiliations:
-
Publ. Info: -
Pages: - Volume / Issue: 36 (16) Sequence Number: - Start / End Page: 1155 - 1160 Identifier: ISBN: 1474-6670