at Google ScholarTitle data
Ou, Ruchuan ; Januzi, Learta ; Schießl, Jonas ; Baumann, Michael Heinrich ; Grüne, Lars ; Faulwasser, Timm:
PolyOCP.jl : A Julia Package for Stochastic OCPs and MPC.
Hamburg
,
2025
. - 8 p.
DOI: https://doi.org/10.48550/arXiv.2511.19084
Project information
| Project title: |
Project's official title Project's id Stochastische Optimale Steuerung und MPC - Dissipativität, Risiko und Regelgüte 499435839 Kooperation in der verteilten modell-prädiktiven Regelung: Die Antizipation, die Reduktion und die Skalierung von Konsensus-Restriktionen 527447339 |
|---|---|
| Project financing: |
Deutsche Forschungsgemeinschaft |
Abstract in another language
The consideration of stochastic uncertainty in optimal and predictive control is a well-explored topic. Recently Polynomial Chaos Expansions (PCE) have seen a lot of considerations for problems involving stochastically uncertain system parameters and also for problems with additive stochastic i.i.d. disturbances. While there exist a number of open-source PCE toolboxes, tailored open-source codes for the solution of OCPs involving additive stochastic i.i.d. disturbances in julia are not available. Hence, this paper introduces the toolbox PolyOCP.jl which enables to efficiently solve stochastic OCPs for a large class of disturbance distributions. We explain the main mathematical concepts between the PCE transcription of stochastic OCPs and how they are provided in the toolbox. We draw upon two examples to illustrate the functionalities of PolyOCP.jl.