Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal. The desulphurization control system, however, is subjected to complex reaction mechanisms and severe disturbances, which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness. To address these challenges, a new robust control scheme based on uncertainty and disturbance estimator (UDE) and model predictive control (MPC) is proposed in this paper. The UDE is used to estimate and dynamically compensate acting disturbances, whereas MPC is deployed for optimal feedback regulation of the resultant dynamics. By viewing the system nonlinearities and unknown dynamics as disturbances, the proposed control framework allows to locally treat the considered nonlinear plant as a linear one. The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small, even in the presence of unmodeled dynamics. In the conducted comparison study, the introduced control scheme outperforms both the standard MPC and PID (proportional-integral-derivative) control strategies in terms of transient performance and robustness. Furthermore, the results reveal that a low-pass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.