PF- MPC: Particle Filter-Model Predictive Control

  • In this article, a new model predictive control approach to nonlinear stochastic systems will be presented. The new approach is based on particle filters, which are usually used for estimating states or parameters. Here, two particle filters will be combined, the first one giving an estimate for the actual state based on the actual output of the system; the second one gives an estimate of a control input for the system. This is basically done by adopting the basic model predictive control strategies for the second particle filter. Later in this paper, this new approach is applied to a CSTR (continuous stirred-tank reactor) example and to the inverted pendulum.

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Metadaten
Author:D. Stahl, J. Hauth
URN (permanent link):urn:nbn:de:hbz:386-kluedo-16888
Serie (Series number):Berichte des Fraunhofer-Instituts für Techno- und Wirtschaftsmathematik (ITWM Report) (201)
Document Type:Report
Language of publication:English
Year of Completion:2011
Year of Publication:2011
Publishing Institute:Fraunhofer-Institut für Techno- und Wirtschaftsmathematik
Tag:Monte Carlo methods; deterministic technical systems ; nonlinear stochastic systems
Faculties / Organisational entities:Fraunhofer (ITWM)
DDC-Cassification:510 Mathematik

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