Distributed Input-Delay Model Predictive Control of Inland Waterways

9 pages•Published: September 20, 2018

Pau Segovia, Lala Rajaoarisoa, Fatiha Nejjari, Eric Duviella and Vicenç Puig

Abstract

Inland waterways are large, complex systems composed of interconnected navigation reaches dedicated mainly to navigation. These reaches are generally characterized by negligible bottom slopes and large time delays. The latter requires ensuring the coordination of the current control actions and their delayed effects in the network. Centralized control strategies are often impractical to implement due to the size of the system. To overcome this issue, a distributed Model Predictive Control (MPC) approach is proposed. The system partitioning is based on a reordering of the optimality conditions matrix, and the control actions are coordinated by means of the Optimality Condition Decomposition (OCD) methodology. The case study is inspired by a real inland waterways system and shows the performance of the approach.

Keyphrases: distributed control, inland waterways, model predictive control, system partitioning

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1893-1901.

Links:	https://easychair.org/publications/paper/PFSQ
	https://doi.org/10.29007/p2cx

BibTeX entry

@inproceedings{HIC2018:Distributed_Input_Delay_Model,
  author    = {Pau Segovia and Lala Rajaoarisoa and Fatiha Nejjari and Eric Duviella and Vicenç Puig},
  title     = {Distributed Input-Delay Model Predictive Control of Inland Waterways},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/PFSQ},
  doi       = {10.29007/p2cx},
  pages     = {1893-1901},
  year      = {2018}}

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