Negotiation and learning in distributed MPC of large scale systems
Document typeConference report
PublisherIEEE Press. Institute of Electrical and Electronics Engineers
Rights accessOpen Access
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.
CitationJavalera, V.; Morcego, B.; Puig, V. Negotiation and learning in distributed MPC of large scale systems. A: American Control Conference. "Proceedings of the 2010 American Control Conference". Baltimore: IEEE Press. Institute of Electrical and Electronics Engineers, 2010, p. 3168-3173.
- SAC - Sistemes Avançats de Control - Ponències/Comunicacions de congressos 
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos 
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos 
- SIC - Sistemes Intel·ligents de Control - Ponències/Comunicacions de congressos