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Gasussian process model based predictive control

2004

Conference Paper

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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.

Author(s): Kocijan, J. and Murray-Smith, R. and Rasmussen, CE. and Girard, A.
Journal: Proceedings of the ACC 2004
Pages: 2214-2219
Year: 2004
Day: 0

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

Event Name: Proceedings of the ACC 2004

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

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BibTex

@inproceedings{2363,
  title = {Gasussian process model based predictive control},
  author = {Kocijan, J. and Murray-Smith, R. and Rasmussen, CE. and Girard, A.},
  journal = {Proceedings of the ACC 2004},
  pages = {2214-2219},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2004}
}