Identification and estimation of valve stiction by the use of a smoothed model

Stiction in control valves is considered one of the most common causes of poor performance in control loops. Thus, non-invasive, reliable and efficient methods which can detect and quantify this malfunction are highly desirable in the process industry. Under the framework of Hammerstein model identification and nonlinear optimization, this paper proposes an approach to estimate stiction amount on the basis of a recently proposed smoothed model. One of the motivations of the work is to improve the performance of a stiction unaware model predictive controller which exhibits sustained oscillations in the presence of valve stiction. By augmenting process model with the identified valve dynamics, the controller is turn to a stiction embedding formulation which can actually remove fluctuations and then guarantees good set-point tracking. Applications to simulation case studies and industrial loops are used to demonstrate the validity of the proposed method. Results are compared with a standard grid-search approach and other identification techniques of the literature.