Distributed State Estimation Based Distributed Model Predictive Control

In this work, we consider output-feedback distributed model predictive control (DMPC) based on state estimation with bounded process disturbances and output measurement noise. Specifically, a scheme observer-enhanced moving horizon (DMHE) is considered for purposes. The DMHE ensures that the estimates of system reach small neighborhood actual values quickly then maintain within neighborhood. This implies error bounded. Based provided by DMHE, DMPC algorithm developed Lyapunov techniques. proposed design, are evaluated synchronously every sampling time. applied to simulated chemical simulation results show applicability effectiveness approach.