Enhancing MPC formulations by identification and estimation of valve stiction. (September 2019)

• Record Type: Journal Article
• Authors: Bacci di Capaci, Riccardo; Vaccari, Marco; Scali, Claudio; Pannocchia, Gabriele
• Is Part Of: Journal of process control. Volume 81(2019)
• Page Start: 31
• Page End: 39
• Publication Date: 2019-09
• Subjects: Model predictive control -- Control valves -- Static friction -- Stiction modeling and estimation -- Numerical optimization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.05.020
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Nonlinear optimization method for stiction quantification and process identification. Hammerstein framework and a smoothed stiction model are deployed. The proposed approach is proved fast and efficient over a grid-search method. MPC model is augmented with the identified valve stiction dynamics. Stiction aware MPC regulators outperform the traditional formulation. Abstract: A common source of poor control performance in industrial processes is represented by stiction in control valves, which often induces offset, oscillating behavior, and even loss of stability. Recent studies have investigated the effectiveness of embedding stiction models into model predictive controller (MPC) schemes, moving from stiction unaware to different stiction aware formulations, which help to remove fluctuations and may guarantee higher set-point tracking ability. To this aim, along with the process model the controller needs to use a dynamic model of sticky valves. This paper proposes an efficient, computational approach to obtain both valve and process dynamics, under the framework of Hammerstein system identification, which is based on nonlinear, gradient-based, numerical optimization. In order to improve the computational behavior and effectiveness of the methodology, a recently proposed smoothed model of stiction is deployed. The proposed methodology is validated in several (single-input single-output, and multivariable) examples, where the effectiveness of the obtained stiction aware MPC regulator is also evaluated against a stiction unaware counterpart.