Quadrature-based scenario tree generation for Nonlinear Model Predictive Control

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Veröffentlicht in:	IFAC-PapersOnLine 47(2014), 3, Seite 11087-11092
Personen und Körperschaften:	Leidereiter, Conrad (VerfasserIn), Potschka, Andreas (VerfasserIn), Bock, Hans Georg (VerfasserIn)
Titel:	Quadrature-based scenario tree generation for Nonlinear Model Predictive Control/ Conrad Leidereiter, Andreas Potschka, Hans Georg Bock
Format:	E-Book-Kapitel
Sprache:	Englisch
veröffentlicht:	2014
Gesamtaufnahme:	Internationale Förderung für Automatische Lenkung: IFAC-PapersOnLine, 47(2014), 3, Seite 11087-11092 , volume:47
Schlagwörter:	Monte-Carlo controller evaluation Nonlinear Model Predictive Control Robust Optimization Scenario Trees Sparse grids
Quelle:	Verbunddaten SWB Lizenzfreie Online-Ressourcen

Details
Zusammenfassung:	A relatively recent approach for robust Nonlinear Model Predictive Control (NMPC) is based on scenario trees with a so-called recourse formulation. This approach is of interest, because it is less conservative than worst-case robustification approaches. A major challenge when using scenario trees for robust NMPC is the large number of scenarios, which grows exponentially. This exponential growth quickly becomes a bottleneck for the computational costs, which need to stay within bounds that permit real-time applicability. We present how to generate scenarios based on a quadrature rule for the expectation value of an arbitrary economic objective function. The use of sparse grids for the quadrature of the high-dimensional stochastic integrals yields a drastically smaller number of scenarios than the tensor grid approaches used so far. We compare the performance of several robust NMPC approaches for a distillation column with three normally distributed uncertain parameters within a simulated Monte-Carlo controller testbed.
Beschreibung:	Available online 25 April 2016 Gesehen am 29.01.2018
Umfang:	6
ISSN:	2405-8963
DOI:	10.3182/20140824-6-ZA-1003.02535