Gaussian process model based predictive control

Kocijan, J., Murray-Smith, R., Rasmussen, C.E. and Girard, A. (2004) Gaussian process model based predictive control. In: American Control Conference, Boston, Massachusetts, 30 June - 2 July 2004, pp. 2214-2219. ISBN 0780383354

Abstract

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear 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 paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick
• Authors: Kocijan, J., Murray-Smith, R., Rasmussen, C.E., and Girard, A.
• Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• ISSN: 0743-1619
• ISBN: 0780383354
• Copyright Holders: Copyright © 2004 Institute of Electrical and Electronics Engineers (IEEE)
• First Published: First published in Proceedings of the 2004 American Control Conference
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher