User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse




Conference Paper

Predictive control with Gaussian process models


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

There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available

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).

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DDAC-F
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.