Stiction parameter identification for pneumatic valves with a simultaneous approach

Abstract

In industrial processes, stiction in control valves is a common cause of degradation in control loop performance. It is important to identify and establish reliable stiction models to enhance control loop performance. This study focuses on constructing a precise model of the pneumatic control valve using the LuGre friction model. To improve the model, a smooth function based on probability density is introduced to alleviate the LuGre friction model. The maximum error resulting from this smoothing process is also analyzed. To estimate the valve stiction parameters, the direct transcription method is utilized to convert the problem from systems of ordinary differential equations into a nonlinear programming problem. The interior point method is then used to solve this problem. Furthermore, the estimability of the parameters is analyzed based on the reduced Hessian matrix before estimation. Numerical results demonstrate that the proposed approach in this study effectively estimates the stiction parameters of pneumatic control valves.