Predictive control with Gaussian process models

Kocijan, J., Murray-Smith, R. , Rasmussen, C.E. and Likar, B. (2003) Predictive control with Gaussian process models. In: Zajc, B. and Tkalcic, M. (eds.) Proceedings of the IEEE Region 8 EUROCON 2003 Computer as a Tool: 22-24 September 2003, Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia. IEEE Computer Society: Piscataway, N.J., USA, 352 -356. ISBN 9780780377639

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This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. 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. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.

Item Type:Book Sections
Keywords:Gaussian process model, black-box identification, constraint optimisation, control signal, model-based predictive control, nonlinear control, nonlinear dynamic system, nonparametric modelling approach, probabilistic modelling approach, Gaussian processes, nonlinear control systems, predictive control
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Authors: Kocijan, J., Murray-Smith, R., Rasmussen, C.E., and Likar, B.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Publisher:IEEE Computer Society

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