Mixed-level iteration schemes for nonlinear model predictive control

• Verfasst von: Frasch, Janick [VerfasserIn]
• Verfasst von: Wirsching, Leonard [VerfasserIn]
• Verfasst von: Sager, Sebastian [VerfasserIn]
• Verfasst von: Bock, Hans Georg [VerfasserIn]
• Titel: Mixed-level iteration schemes for nonlinear model predictive control
• Verf.angabe: Janick V. Frasch, Leonard Wirsching, Sebastian Sager, Hans Georg Bock
• Umfang: 7 S.
• Fussnoten: Available online 21 April 2016 ; Gesehen am 29.01.2018
• Titel Quelle: Enthalten in: Internationale Förderung für Automatische Lenkung: IFAC-PapersOnLine
• Jahr Quelle: 2012
• Band/Heft Quelle: 45(2012), 17, S. 138-144
• ISSN Quelle: 2405-8963
• ISSN Quelle: 1474-6670
• DOI: doi:10.3182/20120823-5-NL-3013.00085
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1567767966

Abstract

In general, numerical schemes for nonlinear model predictive control (NMPC) require the (approximate) solution of a nonlinear program in each sample for feedback generation. Thus, the application of NMPC to processes that need fast feedback poses a major computational challenge. Recently, new multi-level iteration schemes have been proposed, extending the well-known idea of real-time iterations. These algorithms take into account different time scales inherent in the dynamic model by updating the data of the feedback-generating quadratic program (QP), i.e., Hessians and Jacobians, gradients, and constraint residuals, on different levels. In this contribution we consider new mixed-level updates of the QP data, which interval-wise apply different update levels. In particular, we apply higher-level updates more frequently on the first intervals of the control horizon, given their importance in the context of model predictive control in general. Targeting at modern computers withmulti-core processing units, we describe an efficient parallel implementation of the mixed-level iteration approach and apply it to a benchmark problem from automotive engineering.