Adaptive, Cautious, Predictive control with Gaussian Process Priors
2003
Conference Paper
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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.
Author(s): | Murray-Smith, R. and Sbarbaro, D. and Rasmussen, CE. and Girard, A. |
Journal: | Proceedings of the 13th IFAC Symposium on System Identification |
Pages: | 1195-1200 |
Year: | 2003 |
Month: | August |
Day: | 0 |
Editors: | Van den Hof, P., B. Wahlberg and S. Weiland |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Proceedings of the 13th IFAC Symposium on System Identification |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @inproceedings{2316, title = {Adaptive, Cautious, Predictive control with Gaussian Process Priors}, author = {Murray-Smith, R. and Sbarbaro, D. and Rasmussen, CE. and Girard, A.}, journal = {Proceedings of the 13th IFAC Symposium on System Identification}, pages = {1195-1200}, editors = {Van den Hof, P., B. Wahlberg and S. Weiland}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = aug, year = {2003}, doi = {}, month_numeric = {8} } |