Robust Model Predictive Control Under Saturations and Packet Dropouts With Application to Networked Flotation Processes

This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method.