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

AbstractIn this work, we address distributed model predictive control of nonlinear process systems subject to asynchronous measurements. Assuming that there exists an upper bound on the interval between two successive measurements of the process state, two separate Lyapunovbased model predictive controllers that coordinate their actions and take asynchronous measurements explicitly into account are designed. The proposed distributed control design only requires one directional communication between the two distributed controllers and provides the potential of maintaining stability and performance in the face of new or failing actuators. The results are illustrated through a chemical process example.