Model predictive control from routine plant data. (30th June 2019)

• Record Type: Journal Article
• Authors: Sha'aban, Yusuf A.
• Is Part Of: IFAC journal of systems and control. Volume 8(2019)
• Publication Date: 2019-06-30
• Subjects: Model predictive control (MPC) -- Routine data -- PI -- PID -- Simulated annealing (SA); Automatic control -- Periodicals; Relay control systems -- Periodicals; Embedded computer systems -- Periodicals; Feedback control systems -- Periodicals; Artificial intelligence -- Periodicals; Artificial intelligence; Automatic control; Embedded computer systems; Feedback control systems; Relay control systems; Electronic journals; Periodicals; 629.89
• DOI: 10.1016/j.ifacsc.2019.100050
• Languages: English
• ISSNs: 2468-6018

Abstract

Abstract: Advanced control strategies such as Model Predictive Control (MPC) offer a lot of benefits when applied to suitable processes. However, their application has been limited due to a number of reasons including the cost of implementation and unavailability in off-the-shelf control equipment. This paper presents a method of designing MPC controllers from historic plant data. Motivated by the concept of closed-loop identification, the developed method reduces the amount of engineering effort and cost associated with the tuning of MPC to replace an existing Proportional Integral/Proportional Integral Derivative (PI/PID) Controller. Simulation studies were carried out and the MPC parameters obtained were close to the true values. The resulting MPC controllers also had a performances similar to the existing PI controllers. These performances were easily improved using only a single tuning parameter.