Neighbor-communication Model Predictive Control and HVAC systems

Presented in this paper is a variation of the Distributed Model Predictive Control (DMPC) algorithm that converges to the centralized MPC solution for a steady state target optimization with a minimum of communication between plants. This variation, referred to as neighbor-communication MPC, leverages the network topology frequently found in large building HVAC systems; namely, a series of plants where the output of each is a disturbance to other plants. Each plant communicates to its neighbor its intended control action as well as the effects others´ actions will have on itself. By judicious construction of the cost functions, all of the cost information is propagated through the network, allowing a global optimal solution to be reached. The novelty of this approach is that communication between all plants is not necessary to achieve a global optimum, and that changes in one controller do not require changes to all controllers in the network. The approach is demonstrated with a simulated example.