Nonlinear predictive control with a gaussian process model

Kocijan, J., and Murray-Smith, R. (2005) Nonlinear predictive control with a gaussian process model. Lecture Notes in Computer Science, 3355, pp. 185-200. (doi:10.1007/b105497)

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

Abstract

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This chapter illustrates possible application of Gaussian process models within model predictive control. The extra information provided by the Gaussian process model is used in predictive control, where optimization of the control signal takes the variance information into account. The predictive control principle is demonstrated via the control of a pH process benchmark.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Kocijan, J., 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 3355:185-200
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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