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  Gasussian process model based predictive control

Kocijan, J., Murray-Smith, R., Rasmussen, C., & Girard, A. (2005). Gasussian process model based predictive control. In 2004 American Control Conference (pp. 2214-2219). Piscataway, NJ, USA: IEEE Service Center. doi:10.23919/ACC.2004.1383790.

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 Creators:
Kocijan, J, Author
Murray-Smith, R, 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, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coef cients to be optimised. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.

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 Dates: 2005-01
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 2363
DOI: 10.23919/ACC.2004.1383790
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Title: American Control Conference (ACC 2004)
Place of Event: Boston, MA, USA
Start-/End Date: 2004-06-30 - 2004-07-02

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Title: 2004 American Control Conference
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Service Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2214 - 2219 Identifier: ISBN: 0-7803-8335-4