This paper presents three different formulations of MPC to face static friction in control valves for industrial processes. A pure linear formulation, a stiction embedding structure, and a stiction inversion controller are designed. The controllers are derived for SISO systems with linear process dynamics, where valve stiction is the only nonlinearity present in the control loop. A novel smoothed stiction model is introduced to improve and fasten the dynamic optimization module of stiction embedding MPC. A stiction compensation method is revised and used as a warm-start to build a suitable trajectory for the predictive controller. The different MPC formulations are tested and compared on some simulation examples
Valve stiction is one of the most common causes of poor performance in control loops. This paper presents a procedure which allows stiction quantification. The technique permits one to estimate the unknown real stem position, and moreover, it does not need any process knowledge and requires only the data normally registered in industrial plants. It is pointed out that the real problem consists of the lack of knowledge about the true value of stiction. A general methodology is proposed to discard data for which quantification is very likely to give wrong indications and to restrict its application to appropriate cases. Simulations show that several sources of perturbations can be eliminated, thus improving the reliability of stiction evaluation. Results are confirmed by application to industrial data: a significant number of valves are analyzed for repeated acquisitions before and after plant shutdown. The proposed procedure seems to be a valid methodology to monitor valve stiction and to schedule and check valve maintenance
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 twomove 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.
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