A Cooperative Distributed Model Predictive Control for Nonlinear Systems with Automatic Partitioning

Abstract

Abstract Distributed Model Predictive Control (DMPC) algorithms in which each local controller optimizes a global cost function are known as cooperative. This work investigates a cooperative DMPC coupled to an automatic partitioning of the nonlinear system. The local decomposition used is based on the directed graph of the effects on control variables from the space of inputs through a successive linearization of the plant model at each sampling instant. The proposed strategy was applied to a benchmark plant and compared to centralized controller.