@article{scholars710,
            year = {2009},
         journal = {Chemical Product and Process Modeling},
          number = {3},
            note = {cited By 13},
          volume = {4},
           title = {MIQP-based MPC in the presence of control valve stiction},
             doi = {10.2202/1934-2659.1397},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-69949143156&doi=10.2202\%2f1934-2659.1397&partnerID=40&md5=2fdd51a74972228ca6ed6d1616f39db2},
        keywords = {Apriori; Closed-loop performance; Control valve stiction; Control valves; Equipment wear; Fluid catalytic cracking unit; In-control; In-process; Industrial case study; MIQP-based MPC; Mixed integer quadratic programming; Model predictive controllers; Non-Linearity; Process Variables; Product quality; Robustness; Simulated data; Simulation studies; Standing problems; Stiction models; Closed-loop performance; Control valves; Fluid catalytic cracking unit; Industrial case study; MIQP-based MPC; Mixed integer quadratic programming; Model predictive controllers; Simulation studies, Fluid catalytic cracking; Integer programming; Predictive control systems; Robust control; Robustness (control systems); Safety valves; Stiction; System stability; Catalytic cracking; Computer simulation; Fluid catalytic cracking; Integer programming; Predictive control systems; Quadratic programming; Quality control; Robustness (control systems); Safety valves; System stability, Model predictive control; Stiction},
          author = {Zabiri, H. and Samyudia, Y.},
        abstract = {Stiction in control valve is the most common and long standing problem in process industry, resulting in oscillations in process variables which subsequently lower product quality and productivity, accelerates equipment wear and tear, or leads to system instability. In this paper, we apply the Mixed-Integer Quadratic Programming (MIQP)-based Model Predictive Controller (MPC) that was originally developed for system with backlash, to the system with the control valve stiction. The objective is to investigate the robustness of the original MIQP-based MPC in the presence of control valve stiction nonlinearity. To have the real stiction model, we apply neural-network modelling from simulated data of real valve stiction and then use it to test the MIQP-based MPC control. Different scenarios of control valve stiction are considered. Simulation studies using an industrial case study of fluid catalytic cracking unit (FCCU) show that, if the approximate dead-band value is known a priori, the MIQP-based MPC can effectively improve the closed-loop performance in the presence of stiction through the reduction of oscillations in the process variables. {\^A}{\copyright} 2009 The Berkeley Electronic Press. All rights reserved.},
            issn = {19342659}
}