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Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations

Weissel, Florian; Huber, Marco F.; Hanebeck, Uwe D.

In this paper, a framework for stochastic Nonlinear Model Predictive Control (NMPC) that explicitly incorporates the noise influence on systems with continuous state spaces is introduced. By the incorporation of noise, which results from uncertainties during model identification and measurement, the quality of control can be significantly increased. Since stochastic NMPC requires the prediction of system states over a certain horizon, an efficient state prediction technique for nonlinear noise-affected systems is required. This is achieved by using transition densities approximated by axis-aligned Gaussian mixtures together with methods to reduce the computational burden. A versatile cost function representation also employing Gaussianmixtures provides an increased freedom of modeling. Combining the rediction technique with this value function representation allows closed-form calculation of the necessary optimization problems arising from stochastic NMPC. The capabilities of the framework and especially the benefits that can be gained by considering the noise in the controller are illustrated by the example of a mobile robot following a given path.

DOI: 10.1007/978-3-540-85640-5-18
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Buchaufsatz
Publikationsjahr 2009
Sprache Englisch
Identifikator ISBN: 978-3-540-85639-9
KITopen-ID: 1000029740
Erschienen in Informatics in Control, Automation and Robotics
Verlag Springer Verlag
Seiten 239-252
Serie Lecture Notes in Electrical Engineering ; 24
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