KIT | KIT-Bibliothek | Impressum | Datenschutz

A Nonlinear Model Predictive Control Framework Approximating Noise Corrupted Systems with Hybrid Transition Densities

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


In this paper, a framework for Nonlinear Model Predictive Control (NMPC) for heavily noise-affected systems is presented. Within this framework, the noise influence, which originates from uncertainties during model identification or measurement, is explicitly considered. This leads to a significant increase in the control quality. One part of the proposed framework is the efficient state prediction, which is necessary for NMPC. It is based on transition density approximation by hybrid transition densities, which allows efficient closed-form state prediction of time-variant nonlinear systems with continuous state spaces in discrete time. Another part of the framework is a versatile value function representation using Gaussian mixtures, Dirac mixtures, and even a combination of both. Based on these methods, an efficient closed-form algorithm for calculating an approximate value function of the NMPC optimal control problem employing dynamic programming is presented. Thus, also very long optimization horizons can be used and furthermore it is possible to calculate the value function off-line, which reduces the on-line computational burden significantly. ... mehr

Volltext §
DOI: 10.5445/IR/1000034825
DOI: 10.1109/CDC.2007.4434444
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2007
Sprache Englisch
Identifikator ISBN: 978-1-4244-1497-0
KITopen-ID: 1000034825
Erschienen in Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), New Orleans, Louisiana, USA, December, 2007
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 3661-3666
Nachgewiesen in Dimensions
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page