Disturbance Rejection Predictive Control for Flue Gas Desulfurization System

The flue gas desulfurization system is vulnerable to various unknown or unmeasured disturbances. In reality, the type of disturbance is usually unknown or multiple disturbances occur simultaneously in the system. To cope with this issue, this paper proposes the disturbance rejection predictive control strategy based on the new augmented state space model. The new state space model is firstly augmented to reduce the modeling error and be convenient for practical use. To detect the type of disturbance, the disturbance model bank is then established and state estimation is applied to estimate the unknown disturbance model. Furthermore, the method of disturbance model weighting is provided. Finally, the objective function is modified to cope with the continuous effects of the disturbances. Based on the above procedure, a model predictive controller is designed. The validity of the strategy is demonstrated by simulation results.