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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



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ID: 232593.0, MPI für biologische Kybernetik / Biologische Kybernetik
Gaussian process model based predictive control
Authors:Kocijan, J.; Murray-Smith, R.; Rasmussen, C.E.; Girard, A.
Date of Publication (YYYY-MM-DD):2004
Title of Proceedings:Proceedings of the ACC 2004
Start Page:2214
End Page:2219
Physical Description:6
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2363
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