A double-layer model predictive control algorithm for valve position control systems

Abstract

Valve position control systems have the characteristics of combining static and dynamic, as well as fast and slow, which enables the system’s quick response while maintaining some economic performance. However, there is further potential to optimize the economic performance of the valve position control system under conventional control algorithms. This article proposes an improved double-layer model predictive control algorithm for the valve position control system to exploit the potential economic performance while satisfying control goals. In the steady-state optimization layer of the improved double-layer structural model predictive control, the ideal residence point of the key manipulated variable is calculated from the set point of the controlled variable and the steady-state model to characterize the coupling relationship between the primary and secondary manipulated variables in the valve position control system, which realizes the successive actuation feature between the manipulated variables. In order to improve the response speed of the system, the dynamic control layer adopts a two-mode switching strategy, where the mode switching symbol is the time at which the controlled variable first approaches a given steady-state threshold value. Based on the feedback from the dynamic control layer, the steady-state optimization layer optimizes set points of controlled and manipulated variables that can handle disturbances entered into the process at any time to improve the economic performance of valve position control systems. This article implements classical control methods via advanced process strategies to consider the optimal control of processes from a superior perspective.