Non-linear model predictive control for models with local information and uncertainties-Reference-Cited by-同舟云学术

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

Published:2008-12 Issue:5 Volume:30 Page:371-396
ISSN:0142-3312
Container-title:Transactions of the Institute of Measurement and Control
language:en
Short-container-title:Transactions of the Institute of Measurement and Control

Author:

Ažman Kristjan¹,Kocijan Juš²

Affiliation:

1. Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia

2. Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia, , University of Nova Gorica, Vipavska 13, SI-5000 Nova Gorica, Slovenia

Abstract

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.

Publisher

SAGE Publications

Subject

Instrumentation

Link

http://journals.sagepub.com/doi/pdf/10.1177/0142331208095433

Reference21 articles.

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