Self-tuning control of non-linear systems using gaussian process prior models

Sbarbaro, D., and Murray-Smith, R. (2005) Self-tuning control of non-linear systems using gaussian process prior models. Lecture Notes in Computer Science, 3355, pp. 140-157. (doi:10.1007/b105497)

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Publisher's URL: http://dx.doi.org/10.1007/b105497

Abstract

Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal (caution) in areas of high uncertainty. As a consequence, the controller has dual features, since it both tracks a reference signal and learns a model of the system from observed responses. The general method and its unique features are illustrated on simulation examples.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Sbarbaro, D., and Murray-Smith, R.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:1611-3349
Copyright Holders:Copyright © 2005 Springer
First Published:First published in Lecture Notes in Computer Science 3355:140-157
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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