Sequential and Iterative Distributed Model Predictive Control of Nonlinear Process Systems Subject to Asynchronous Measurements

Abstract

In this work, we focus on sequential and iterative distributed model predictive control (DMPC) of large scale nonlinear process systems subject to asynchronous measurements. Assuming that there is an upper bound on the maximum interval between two consecutive asynchronous measurements, we design DMPC schemes that take into account asynchronous feedback explicitly via Lyapunov techniques. Sufficient conditions under which the proposed distributed control designs guarantee that the states of the closed-loop system are ultimately bounded in regions that contain the origin are provided. The theoretical results are illustrated through a catalytic alkylation of benzene process example.