Negotiation and learning in distributed MPC of large scale systems

Conference Article


American Control Conference (ACC)





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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.


predictive control.

Author keywords

cooperative systems, distributed control, model predictive control, multi agent systems, negotiation, reinforcement learning

Scientific reference

V. Javalera, B. Morcego and V. Puig. Negotiation and learning in distributed MPC of large scale systems, 2010 American Control Conference, 2010, Baltimore MD, USA, pp. 3168-3173, IEEE.