Control of Inverse Response Process using Model Predictive Controller (Simulation)

• Main Author: Fuat, Fawwaz
• Format: Final Year Project
• Language: English
• Published: UNIVERSITI TEKNOLOGI PETRONAS 2012
• Subjects: TP Chemical technology
• Online Access: http://utpedia.utp.edu.my/6148/1/FYP%202_11930.pdf; http://utpedia.utp.edu.my/6148/

Model predictive control is an important model-based control strategy devised for large multiple-input, multiple-output control problems with inequality constraints on the input and outputs. Applications typically involve two types of calculations: (1) a steady-state optimization to determine the optimum set points for the control calculations, and (2) control calculations to determine the input changes that will drive the process to the set points. The success of model-based control strategies such as MPC depends strongly on the availability of a reasonably accurate process model. Consequently, model development is the most critical step in applying MPC. As Rawlings (2000) has noted, “feedback can overcome some effects of poor model, but starting with a poor process model is a kind to driving a car at night without headlight.” Finally the MPC design should be chosen carefully. Model predictive control has had a major impact on industrial practice, with over 4500 applications worldwide. MPC has become the method of choice for difficult control problems in the oil refining and petrochemical industries. However, it is not a panacea for all difficult control problem(Shinkey, 1994; Hugo, 2000). Furthermore, MPC has had much less impact in the order process industries. Performance monitoring of MPC systems is an important topic of current research interest.