Towards turnpike-based performance analysis of risk-averse stochastic predictive control
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908293971050496 |
|---|---|
| author | Schießl, Jonas Ou, Ruchuan Baumann, Michael H. Faulwasser, Timm Grüne, Lars |
| author_facet | Schießl, Jonas Ou, Ruchuan Baumann, Michael H. Faulwasser, Timm Grüne, Lars |
| contents | In this paper, we present performance estimates for stochastic economic MPC schemes with risk-averse cost formulations. For MPC algorithms with costs given by the expectation of stage cost evaluated in random variables, it was recently shown that the guaranteed near-optimal performance of abstract MPC in random variables coincides with its implementable variant coincide using measure path-wise feedback. In general, this property does not extend to costs formulated in terms of risk measures. However, through a turnpike-based analysis, this paper demonstrates that for a particular class of risk measures, this result can still be leveraged to formulate an implementable risk-averse MPC scheme, resulting in near-optimal averaged performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards turnpike-based performance analysis of risk-averse stochastic predictive control Schießl, Jonas Ou, Ruchuan Baumann, Michael H. Faulwasser, Timm Grüne, Lars Optimization and Control In this paper, we present performance estimates for stochastic economic MPC schemes with risk-averse cost formulations. For MPC algorithms with costs given by the expectation of stage cost evaluated in random variables, it was recently shown that the guaranteed near-optimal performance of abstract MPC in random variables coincides with its implementable variant coincide using measure path-wise feedback. In general, this property does not extend to costs formulated in terms of risk measures. However, through a turnpike-based analysis, this paper demonstrates that for a particular class of risk measures, this result can still be leveraged to formulate an implementable risk-averse MPC scheme, resulting in near-optimal averaged performance. |
| title | Towards turnpike-based performance analysis of risk-averse stochastic predictive control |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2504.00701 |