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Conference Paper

Adaptive, Cautious, Predictive control with Gaussian Process Priors

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

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

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Murray-Smith, R., Sbarbaro D, Rasmussen, C., & Girard, A. (2003). Adaptive, Cautious, Predictive control with Gaussian Process Priors. In Proceedings of the 13th IFAC Symposium on System Identification (pp. 1195-1200).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DBF8-F
Abstract
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.