Gasussian process model based predictive control

Kocijan, J; Murray-Smith, R; Rasmussen, CE; Girard, A

doi:10.23919/ACC.2004.1383790

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Gasussian process model based predictive control

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Rasmussen, CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F39B-1

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.