Unlocking feedforward capabilities in Model Predictive Control algorithms to deal with measurable disturbances
For process control practitioners, this work addresses a known limitation of MPC in handling measurable disturbances, but the approach is incremental as it builds on existing MPC formulations.
The paper proposes a dual-control framework for MPC that computes tracking and feedforward actions simultaneously, enabling full rejection of measurable disturbances without removing control effort penalty. Simulations on a reverse osmosis process show improved disturbance rejection compared to standard MPC and classical feedforward schemes.
Disturbance rejection is a central objective in process control, particularly when measurable disturbances can be exploited through feedforward action. Although Model Predictive Control (MPC) naturally incorporates disturbance models and prediction capabilities, standard formulations cannot achieve complete disturbance rejection since the cost function penalises control effort. This limitation prevents MPC from reproducing the behaviour of classical feedforward compensators. This work proposes a novel framework to embed true feedforward capabilities within MPC without removing the control effort penalty. The approach introduces a dual-control structure in which two control actions are computed simultaneously: a tracking-oriented action addressing set-point tracking and robustness, and a feedforward-oriented action dedicated to disturbance rejection. Both contributions are combined into a single control signal on which the process constraints are explicitly enforced. The feedforward-oriented action is formulated without penalising control effort, enabling full compensation of measurable disturbances. The methodology is developed for Dynamic Matrix Control (DMC), Generalised Predictive Control (GPC), and state-space MPC. Its effectiveness is demonstrated through simulation studies, including comparisons with standard MPC and classical feedforward schemes. A case study based on a reverse osmosis process shows that the proposed approach improves disturbance rejection while preserving constraint handling and overall control performance.