Stiction compensation via model predictive control

Abstract

In this work, we develop a model predictive control (MPC) strategy incorporating stiction dynamics, input rate of change constraints, and actuation magnitude constraints as a stiction compensation methodology. Stiction is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. In this work, we develop an MPC formulation including detailed valve dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions from the valve due to stiction. Using a chemical process example with an economic model predictive controller (EMPC), we demonstrate the selection of appropriate constraints for the proposed method and show that the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC improves its selection of control actions so that the valves are able to reach the set-points requested by the EMPC and to meet operating constraints.