Partitioning for Distributed Model Predictive Control of Nonlinear Processes

Abstract A distributed model predictive control (DMPC) strategy brings interesting features of topology, flexibility and maintenance to large-scale nonlinear systems. This work presents contributions in the study of distributed controllers for nonlinear and large-scale systems. Two types of distributed predictive control based on model (DMPC) are proposed: non-cooperative locally linearized DMPC and cooperative locally linearized DMPC. The decomposition is performed based on a local linearized version of the process model by using local matrices representing interactions between controlled outputs, states and inputs. The proposed strategy was successfully evaluated and compared to the centralized control strategy.