Abstract: In this study, we propose a two-layered control framework integrating event-triggered dynamic real-time optimization (D-RTO) and model predictive control (MPC), addressing the issue of infeasibility and performance loss caused by uncertainties. The upper layer utilizes economic MPC (EMPC) to minimize an economic cost function, calculating reference trajectories. The lower layer utilizes MPC to steer the plant to track the reference trajectories. A triggering criterion based on cost function is proposed to compensate for performance loss in time, without waiting for the next D-RTO optimization. When the deviation between these two layers’ cost function exceeds a pre-set threshold, the EMPC is triggered to excute optimization and update the reference trajectory based on the current system states. Feasibility and closed-loop stability of the proposed two-layered control framework have also been proved. Simulations demonstrate its effectiveness.

Key words: model predictive control (MPC), two-layered control framework, dynamic real-time optimization (D-RTO), event-triggered, uncertainty