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)

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.