Handling Communication Disruptions in Distributed Model Predictive Control

In this work, we deal with distributed model predictive control (DMPC) of nonlinear systems with communication disruptions between the distributed controllers. Specifically, we focus on the design of DMPC systems that take into account communication channel noise and data losses between the distributed controllers explicitly. In contrast to most of the existing DMPC methods which assume flawless communication, we employ a specific channel model to consider a number of realistic data transmission scenarios. In order to ensure the stability of the closed-loop system under communication disruptions, each model predictive controller utilizes a stability constraint which is based on a suitable Lyapunov-based controller. The theoretical results are demonstrated through a nonlinear chemical process example.