A key issue in distributed MPC control of Large Scale Systems (LSS) is how shared variables among the different MPC controller in charge of controlling each system partition (subsystems) are handled. When these connections represent control variables, the distributed control has to be consistent for both subsystems and the optimal value of these variables will have to accomplish a common goal. In order to achieve this, the present work combines ideas from Distributed Artificial Intelligence (DAI), Reinforcement Learning (RL) and Model Predictive Control (MPC) in order to provide an approach based on negotiation, cooperation and learning techniques. Results of the application of this approach to a small drinking water network show that the resulting trajectories of the levels in tanks (control variables) can be acceptable compared to the centralized solution. The application to a real network (the Barcelona case) is currently under development. ; Peer Reviewed ; Postprint (author’s final draft)
Negotiation and learning in distributed MPC of large scale systems
2010-01-01
Sonstige
Elektronische Ressource
Englisch
Àrees temàtiques de la UPC::Economia i organització d'empreses::Gestió i direcció , Production management , predictive control PARAULES AUTOR:cooperative systems , distributed control , model predictive control , multi agent systems , negotiation , reinforcement learning , Producció -- Direcció i administració , Classificació INSPEC::Control theory::Predictive control
DDC: | 629 |
Online Contents | 2016
|Online Contents | 2001
A distributed algorithm for operating large-scale ridesourcing systems
Elsevier | 2021
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