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Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities

Hekler, Achim; Lyons, Daniel; Noack, Benjamin; Hanebeck, Uwe D.


In Model Predictive Control, the quality of control is highly dependent upon the model of the system under control. Therefore, a precise deterministic model is desirable. However, in real-world applications, modeling accuracy is typically limited and systems are generally affected by disturbances. Hence, it is important to systematically consider these uncertainties and to model them correctly. In this paper, we present a novel Nonlinear Model Predictive Control method for systems affected by two different types of perturbations that are modeled as being either stochastic or unknown but bounded quantities. We derive a formal generalization of the Nonlinear Model Predictive Control principle for considering both types of uncertainties simultaneously, which is achieved by using sets of probability densities. In doing so, a more robust and reliable control is obtained. The capabilities and benefits of our approach are demonstrated in real-world experiments with miniature walking robots.

Volltext §
DOI: 10.5445/IR/1000035048
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-5362-7
KITopen-ID: 1000035048
Erschienen in Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010), Yokohama, Japan, 8-10 Sept. 2010
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1337-1342
Nachgewiesen in Dimensions
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