Two‐layer model predictive control for chemical process model with integrating controlled variables

Abstract

This paper proposes an overall solution to the two‐layer model predictive control (MPC) for the integrating controlled variables in the process model. The scheme includes three modules, that is, the open‐loop prediction module, the steady‐state target calculation (SSTC) module, and the dynamic control module. Based on the real‐time output measurements and past inputs, the open‐loop prediction module predicts the future outputs in the presence of disturbances. The economic optimization of SSTC is comprised of the feasibility stage and the economics stage, considering constraints of multi‐priority ranks. The dynamic control module receives the steady‐state targets from SSTC and calculates the control signals. The optimization problems of SSTC and dynamic control operate with the same frequency. This overall method guarantees the consistency of three modules with respect to the model, the constraints, and the targets. The simulation example illustrates that steady‐state targets are adjusted dynamically after the occurrence of disturbances, and offset‐free control is achieved.

A weighted local steady‐state determination approach based on the globally optimal economic steady‐states

“…Zheng et al [15] proposed one explicit two-layer model predictive control strategy based on off-line optimization and on-line table look-up. Yang and Ding [16] discussed the integral characteristic in chemical processes and solved it with TLMPC. Hu and Ding [17] proposed one output feedback strategy of TLMPC for fuzzy systems.…”

Multi-Parameter Quadratic Programming Explicit Model Predictive Based Real Time Turboshaft Engine Control

“…MPC originated in the 1970s. Due to its unique advantages, such as the ability to predict future output, process constrained optimization, and the ability to suppress unmodeled dynamic error and environmental disturbance uncertainty, it has been widely discussed by the industry [3][4][5], and has been successfully applied in industrial process control with slow dynamic characteristics such as petroleum smelting and chemical industry [6][7][8]. Currently, all the application research of turboshaft engines that we can find is particularly scarce and most of the model predictive controller designs for aeroengines are focused on solving the model mismatch phenomenon.…”