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



ID: 198176.0, MPI für biologische Kybernetik / Biologische Kybernetik
Adaptive, Cautious, Predictive control with Gaussian Process Priors
Authors:Murray-Smith, R.; Sbarbaro, D.; Rasmussen, C.E.; Girard, A.
Editors:Hof, P. Van den; Wahlberg, B.; Weiland, S.
Date of Publication (YYYY-MM-DD):2003-08
Title of Proceedings:Proceedings of the 13th IFAC Symposium on System Identification
Start Page:1195
End Page:1200
Review Status:not specified
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2316
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