Distributed Model Predictive Control of Nonlinear Systems Subject to Delayed Measurements

In this work, we focus on distributed model predictive control of nonlinear systems subject to delayed measurements. Delayed measurements arise naturally in process control applications when, for example, species concentrations or particle size distributions are measured. We design distributed Lyapunov-based model predictive controllers that coordinate their actions and take delayed measurements explicitly into account. Sufficient conditions under which the proposed distributed control design guarantees that the state of the closed-loop system is ultimately bounded in a region that contains the origin are provided. The applicability and effectiveness of the proposed control method is illustrated through a chemical process example.