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Distributed MPC for large scale systems using agent-based reinforcement learning

Author
Javalera, V.; Morcego, B.; Puig, V.
Type of activity
Presentation of work at congresses
Name of edition
12th IFAC Symposium on Large-Scale Systems: Theory and Applications, 2010
Date of publication
2010
Presentation's date
2010
Book of congress proceedings
Proceedings of 12th IFAC Symposium on Large-Scale Systems: Theory and Applications
First page
1
Last page
6
Repository
http://hdl.handle.net/2117/9208 Open in new window
URL
http://www.ifacpapersonline.com Open in new window
Abstract
In the present work, distributed control and artificial intelligence are combined in a control architecture for Large Scale Systems (LSS). The aim of this architecture is to provide a general structure and methodology to perform optimal control in networked distributed environments where multiple dependencies between sub-systems are found. Often these dependencies or connections represent control variables so the distributed control has to be consistent for both subsystems and the optimal value ...
Keywords
Distributed control Distributed architectures MPC Learning Multi-agent systems
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control
SAC - Advanced Control Systems
SIC - Smart Control Systems

Participants