Towards turnpike-based performance analysis of risk-averse stochastic predictive control

Publikationstyp
Conference Paper
Date Issued
2025-12
Sprache
English
Author(s)
Schießl, Jonas
Ou, Ruchuan
Regelungstechnik E-14
Baumann, Michael
Faulwasser, Timm
Regelungstechnik E-14
Grüne, Lars
TORE-URI
https://hdl.handle.net/11420/61109
Start Page
329
End Page
335
Citation
64th IEEE Conference on Decision and Control, CDC 2025
Contribution to Conference
64th IEEE Conference on Decision and Control, CDC, 2025
Publisher DOI
10.1109/cdc57313.2025.11312994
Scopus ID
2-s2.0-105031894622
Publisher
IEEE
ISBN of container
979-8-3315-2627-6
979-8-3315-2628-3
In this paper, we present performance estimates for stochastic economic MPC schemes with risk-averse cost formulations. For MPC algorithms with costs given by expectations, it was recently shown that the guaranteed near-optimal performance of abstract MPC in random variables coincides with its implementable variant using pathwise 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.
DDC Class
600: Technology
629.8: Control and Feedback Control Systems