On Disturbance Estimation-and Exploitation-Based MPC Design With Application to Level Control System

This article considers the model predictive control (MPC) problem for a class of time-varying systems subject to both disturbances and constraints of states as well as input. Instead of directly negating disturbance by its estimation in feedback control, we exploit the disturbance estimation in the MPC optimization problem for seeking the optimal control input. In particular, the extended state observer (ESO) is constructed to obtain the disturbance estimation to be incorporated into the prediction model. Furthermore, less conservative tightened constraints and terminal constraints with consideration of disturbance estimation are constructed to guarantee robust constraint satisfaction and recursive feasibility. Also, the input-to-state stability (ISS) of the closed-loop system is rigorously proven. Finally, the proposed method is applied to the liquid-level control system. The experimental results demonstrate the effectiveness of our MPC algorithm.