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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: | , , |
| 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
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| Gesamtaufnahme: |
Internationale Förderung für Automatische Lenkung: IFAC-PapersOnLine, 47(2014), 3, Seite 11087-11092
, volume:47 |
| Schlagwörter: | |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| 520 | |a 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. | ||
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| contents | 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. |
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| spelling | Leidereiter, Conrad 1988- VerfasserIn (DE-588)1151454737 (DE-627)101180168X (DE-576)497761874 aut, Quadrature-based scenario tree generation for Nonlinear Model Predictive Control Conrad Leidereiter, Andreas Potschka, Hans Georg Bock, 2014, 6, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Available online 25 April 2016, Gesehen am 29.01.2018, 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., Monte-Carlo controller evaluation, Nonlinear Model Predictive Control, Robust Optimization, Scenario Trees, Sparse grids, Potschka, Andreas 1980- VerfasserIn (DE-588)1019443391 (DE-627)685041166 (DE-576)358073995 aut, Bock, Hans Georg 1948- VerfasserIn (DE-588)1025289927 (DE-627)721988717 (DE-576)370169255 aut, Enthalten in Internationale Förderung für Automatische Lenkung IFAC-PapersOnLine Frankfurt : Elsevier, 2015 47(2014), 3, Seite 11087-11092 Online-Ressource (DE-627)839396090 (DE-600)2839185-8 (DE-576)450759253 2405-8963 nnns, volume:47 year:2014 number:3 pages:11087-11092 extent:6, http://dx.doi.org/10.3182/20140824-6-ZA-1003.02535 Verlag Resolving-System kostenfrei Volltext, http://www.sciencedirect.com/science/article/pii/S147466701643377X Verlag kostenfrei Volltext, http://dx.doi.org/10.3182/20140824-6-ZA-1003.02535 LFER, LFER 2018-03-07T00:00:00Z |
| spellingShingle | Leidereiter, Conrad, Potschka, Andreas, Bock, Hans Georg, Quadrature-based scenario tree generation for Nonlinear Model Predictive Control, 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., Monte-Carlo controller evaluation, Nonlinear Model Predictive Control, Robust Optimization, Scenario Trees, Sparse grids |
| swb_id_str | 497762528 |
| title | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control |
| title_auth | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control |
| title_full | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control Conrad Leidereiter, Andreas Potschka, Hans Georg Bock |
| title_fullStr | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control Conrad Leidereiter, Andreas Potschka, Hans Georg Bock |
| title_full_unstemmed | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control Conrad Leidereiter, Andreas Potschka, Hans Georg Bock |
| title_in_hierarchy | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control / Conrad Leidereiter, Andreas Potschka, Hans Georg Bock, |
| title_short | Quadrature-based scenario tree generation for Nonlinear Model Predictive Control |
| title_sort | quadrature based scenario tree generation for nonlinear model predictive control |
| topic | Monte-Carlo controller evaluation, Nonlinear Model Predictive Control, Robust Optimization, Scenario Trees, Sparse grids |
| topic_facet | Monte-Carlo controller evaluation, Nonlinear Model Predictive Control, Robust Optimization, Scenario Trees, Sparse grids |
| url | http://dx.doi.org/10.3182/20140824-6-ZA-1003.02535, http://www.sciencedirect.com/science/article/pii/S147466701643377X |