Code underlying the publication: A Generalized Partitioning Strategy for Distributed Control

doi:10.4121/90ada13d-a6c9-4e4c-a046-2b984595bcdd.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/90ada13d-a6c9-4e4c-a046-2b984595bcdd
Datacite citation style:
Riccardi, Alessandro; Laurenti, Luca; De Schutter, Bart (2024): Code underlying the publication: A Generalized Partitioning Strategy for Distributed Control. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/90ada13d-a6c9-4e4c-a046-2b984595bcdd.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software

The partitioning problem is a key problem for distributed control techniques. The problem consists in the definition of the subnetworks of a dynamical system that can be considered as individual control agents in the distributed control approach. Despite its relevance and the different approaches proposed in the literature, no generalized technique to perform the partitioning of a network of dynamical systems is present yet. In this article, we introduce a general approach to partitioning for distributed control. This approach is composed by an algorithmic part selecting elementary subnetworks, and by an integer program, which aggregates the elementary components according to a global index. We empirically evaluated our approach on a distributed predictive control problem in the context of power systems, obtaining promising performances in terms of reduction of computation speed and resource cost, while retaining a good level of performance.

history
  • 2024-12-17 first online, published, posted
publisher
4TU.ResearchData
funding
  • CLariNet (grant code 101018826) [more info...] European Research Council
organizations
TU Delft, Faculty of Mechanical Engineering, Delft Center for Systems and Control

DATA

To access the source code, use the following command:

git clone https://data.4tu.nl/v3/datasets/502016d0-e852-4176-8a7e-0e4d8b350440.git

Or download the latest commit as a ZIP.