HD-MPC - Hierarchical and Distributed Model Predictive Control of Large-Scale Systems
From 01-09-2008 to 31-12-2011
Website: http://www.ICT-HD-MPC.eu
Description
Manufacturing systems, power networks, transportation systems, road traffic networks, process plants, and other large-scale networked systems are often composed of multiple subsystems, with many embedded sensors and actuators, and characterised by complex dynamics and mutual influences such that local control decisions have long-range effects throughout the system. This results in a huge number of problems that must be tackled for the design of an overall control system in order to achieve a safe, efficient, and robust operation. Otherwise, serious disasters and malfunctions could occur (such as the breakdown of the power grid in North America and in Italy in 2003).
To deal with these problems and to cope with the complexity of the control task, we propose to use a hierarchical control set-up in which the control tasks are distributed over time and space. In such a set-up, systems of supervisory and strategic functionality reside at higher levels, while at lower levels the single units, or local agents, must guarantee specific operational objectives. At any level, the local agents must negotiate their outcomes and requirements with lower and higher levels. We will develop methods for designing controllers for complex large-scale systems based on such a hierarchical control framework. In particular, we propose to use Model Predictive Control (MPC), which has already proven its usefulness for control of small-scale systems, but which cannot yet be applied to large-scale systems due to computational, coordination, and
communication problems. We will solve these issues and develop new MPC methods for large-scale networked systems, both under normal operation conditions, and in the presence of uncertainty and disturbances. We will perform both fundamental research and more application-oriented research in which the methods developed in the project are applied to case studies and benchmarks provided by the partners from industry.
Team
- Moritz Diehl, Promoter
Financing
Funding: EU Funding - European Funding
Program/Grant Type: EU Other - Other
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.