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Predictive control with Gaussian process models

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Murray-Smith R, Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Kocijan, J., Murray-Smith R, Rasmussen, C., & Likar, B. (2003). Predictive control with Gaussian process models. In Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool (pp. 352-356).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-DDAC-F
Zusammenfassung
This paper describes model-based predictive control based on Gaussian processes.Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. 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. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.