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 fo...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2014
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| In: |
IFAC-PapersOnLine
Year: 2014, Volume: 47, Issue: 3, Pages: 11087-11092 |
| ISSN: | 2405-8963 |
| DOI: | 10.3182/20140824-6-ZA-1003.02535 |
| Online Access: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.3182/20140824-6-ZA-1003.02535 Verlag, kostenfrei, Volltext: http://www.sciencedirect.com/science/article/pii/S147466701643377X 
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| Author Notes: | Conrad Leidereiter, Andreas Potschka, Hans Georg Bock |
| Summary: | 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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| Item Description: | Available online 25 April 2016 Gesehen am 29.01.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2405-8963 |
| DOI: | 10.3182/20140824-6-ZA-1003.02535 |