Model predictive control design for multivariable processes in the presence of valve stiction. (November 2018)

• Record Type: Journal Article
• Authors: Bacci di Capaci, Riccardo; Vaccari, Marco; Pannocchia, Gabriele
• Is Part Of: Journal of process control. Volume 71(2018)
• Page Start: 25
• Page End: 34
• Publication Date: 2018-11
• Subjects: Model predictive control -- Control valves -- Static friction -- Stiction modeling and compensation; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.09.006
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Three different formulations of MPC to handle stiction in control valves. An appropriate inverse model is implemented within the stiction inversion MPC. A suitable compensation method is needed as a warm start in stiction embedded MPC. Suitable choices of objective functions and constraints improve tracking ability. Stiction embedding MPC can ensure tracking performance and stiction compensation. Abstract: This paper presents different formulations of Model Predictive Control (MPC) to handle static friction in control valves for industrial processes. A fully unaware formulation, a stiction embedding structure, and a stiction inversion controller are considered. These controllers are applied to multivariable systems, with linear and nonlinear process dynamics. A semiphysical model is used for valve stiction dynamics and the corresponding inverse model is derived and used within the stiction inversion controller. The two-move stiction compensation method is revised and used as warm-start to build a feasible trajectory for the MPC optimal control problem. Some appropriate choices of objective functions and constraints are used with the aim of improving performance in set-points tracking. The different MPC formulations are reviewed, compared, and tested on several simulation examples. Stiction embedding MPC proves to guarantee good performance in set-points tracking and also stiction compensation, at the expense of a lower robustness with respect to other two formulations.