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The code accompanies the publication "Feedback Linearization based on Gaussian Processes with event-triggered Online Learning" by Jonas Umlauft and Sandra Hirche published in IEEE Transactions on Automatic Control in 2020.

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jumlauft/Safe-Online-Learning-for-Gaussian-Processes

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The provided Matlab code accompanies the following work:

@Article{umlauft2020feedback, author = {Umlauft, Jonas and Hirche, Sandra}, date = {2020}, journaltitle = {IEEE Transactions on Automatic Control ({TAC})}, title = {Feedback Linearization based on {G}aussian Processes with event-triggered Online Learning}, doi = {10.1109/TAC.2019.2958840}, issn = {1558-2523}, pages = {1--16}, url = {https://ieeexplore.ieee.org/document/8930275}, file = {:papers/umlauft2020feedback.pdf:PDF}, }

Please acknowledge the authors in any academic publication that have made use of this code or parts of it by referencing to the paper.

Please send your feedbacks or questions to: jonas.umlauft_at_tum.de

The software makes use of the following software package: GAUSSIAN PROCESS REGRESSION AND CLASSIFICATION Toolbox version 3.6 for GNU Octave 3.2.x and Matlab 7.x The code is released under the FreeBSD License. Copyright (c) 2005-2015 Carl Edward Rasmussen & Hannes Nickisch. All rights reserved. The code and associated documentation is available from http://gaussianprocess.org/gpml/code.

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The code accompanies the publication "Feedback Linearization based on Gaussian Processes with event-triggered Online Learning" by Jonas Umlauft and Sandra Hirche published in IEEE Transactions on Automatic Control in 2020.

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