Convergence analysis and adaptive order selection for the polynomial chaos approach to direct optimal control under uncertainties
We consider the use of the polynomial chaos method to approximate optimal control problems with randomly varying uncertain parameters by deterministic surrogate problems. Our focus is on nonlinear problems that require a high expansion order to give meaningful statements about the uncertainty propag...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
February 11, 2021
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| In: |
SIAM journal on control and optimization
Year: 2021, Volume: 59, Issue: 1, Pages: 509-533 |
| ISSN: | 1095-7138 |
| DOI: | 10.1137/17M1133038 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1137/17M1133038 Verlag, lizenzpflichtig, Volltext: https://epubs.siam.org/doi/10.1137/17M1133038 
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| Author Notes: | Lilli Frison and Christian Kirches |
| Summary: | We consider the use of the polynomial chaos method to approximate optimal control problems with randomly varying uncertain parameters by deterministic surrogate problems. Our focus is on nonlinear problems that require a high expansion order to give meaningful statements about the uncertainty propagation. Their resulting size and complexity pose a computational challenge for traditional optimal control methods. Our contributions include an adaptive optimization strategy which refines the approximation quality separately for each state variable using suitable error estimates. The benefits are twofold: we obtain additional means for solution verification and reduce the computational effort for finding an approximate solution with increased precision, as is highlighted in a numerical case study with two nonlinear real-world problems. The algorithmic contribution is complemented by a convergence proof showing that the optimal control solutions approach the correct solution for increasing expansion orders. |
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| Item Description: | Gesehen am 28.07.2021 |
| Physical Description: | Online Resource |
| ISSN: | 1095-7138 |
| DOI: | 10.1137/17M1133038 |