This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Azman, K. Articles by Kocijan, J. Search for Related Content Social Bookmarking                         What's this?

Non-linear model predictive control for models with local information and uncertainties

Kristjan Aman

Joef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia

Ju Kocijan

Joef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia, jus.kocijan{at}ijs.si, University of Nova Gorica, Vipavska 13, SI-5000 Nova Gorica, Slovenia

Gaussian processes are a probabilistic, non-parametric approach to modelling that allows easy merging of ordinary measured data and local linear models. This can be of particular importance in the identification of non-linear dynamic systems from experimental data, where there is usually more data available around the equilibrium points and only sparse data is available far from them. The utility of the Gaussian process model for predictive control is investigated in this paper and is illustrated on a simple first-order vehicle dynamics.

Key Words: Gaussian process model • linear local models • model uncertainties • non-linear predictive control

Transactions of the Institute of Measurement and Control, Vol. 30, No. 5, 371-396 (2008)
DOI: 10.1177/0142331208095433


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?