Economic model predictive control for achieving offset-free operation performance of industrial constrained systems

Abstract

The optimization and control of large-scale industrial systems are typically based on hierarchical structures. The real-time optimizer (RTO) solves a steady-state setpoint optimal problem in long time-scale, based on a rigorous nonlinear model. The down-layer advanced controller is designed to achieve setpoint tracking based on a linear model to get efficient computation. This paradigm on the one hand lacks consideration about dynamic economic performance, on the other hand leads to the unreachable and inconsistent issues, which cause feasibility problem in the controller and the deviation of the plant operating point, i.e., offset. In this work, a linear offset-free economic model predictive controller (EMPC) is proposed for systems in the presence of model mismatch. The method constituted by a dynamic target optimization (DTO) stage and the following EMPC stage. These two stages work in a bidirectional way. The DTO stage uses the dynamic augmented model to derive a feasible trajectory with the terminal target attainable for down-layer EMPC. The EMPC optimizes the dynamic economic performance in the presence of a contractive energy-like constraint related to the feasible trajectory. Recursive feasibility, input-to-state stability (ISS) and offset-free property are guaranteed. The proposed method is applied to a fluid catalytic cracking (FCC) process. The numerical results demonstrate the effectiveness of the proposed offset-free EMPC for improving the overall economic performance and achieving stability and offset-free property in process control.