Quadrature-based scenario tree generation for Nonlinear Model Predictive Control

• Verfasst von: Leidereiter, Conrad [VerfasserIn]
• Verfasst von: Potschka, Andreas [VerfasserIn]
• Verfasst von: Bock, Hans Georg [VerfasserIn]
• Titel: Quadrature-based scenario tree generation for Nonlinear Model Predictive Control
• Verf.angabe: Conrad Leidereiter, Andreas Potschka, Hans Georg Bock
• Umfang: 6 S.
• Fussnoten: Available online 25 April 2016 ; Gesehen am 29.01.2018
• Titel Quelle: Enthalten in: Internationale Förderung für Automatische Lenkung: IFAC-PapersOnLine
• Jahr Quelle: 2014
• Band/Heft Quelle: 47(2014), 3, S. 11087-11092
• ISSN Quelle: 2405-8963
• ISSN Quelle: 1474-6670
• DOI: doi:10.3182/20140824-6-ZA-1003.02535
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1567762522

Abstract

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 withina simulated Monte-Carlo controller testbed.