Mixed-level iteration schemes for nonlinear model predictive control
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-...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2012
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| In: |
IFAC-PapersOnLine
Year: 2012, Jahrgang: 45, Heft: 17, Pages: 138-144 |
| ISSN: | 2405-8963 |
| DOI: | 10.3182/20120823-5-NL-3013.00085 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3182/20120823-5-NL-3013.00085 Verlag, kostenfrei, Volltext: http://www.sciencedirect.com/science/article/pii/S1474667016314409 
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| Verfasserangaben: | Janick V. Frasch, Leonard Wirsching, Sebastian Sager, Hans Georg Bock |
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| 520 | |a 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 with multi-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. | ||
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