Application of Model Predictive Control (Mpc) Tuning Strategy in Multivariable Control of Distillation Column

Abstract

IntroductionPredictive control is now one of the most widely used advanced control methods in industry, especially in the control of processes that are constrained, multivariable and uncertain. A large number of implementation algorithms, including industrial predictive control applications (Qin and Badgwell, 2003) have appeared in the literature.The cornerstone of MPC is the model (Clarke, 1996). It cause MPC is called MBPC (model-based predictive control). MPC uses models in 2 ways: using a reliable model to predict effect of past control moves on P (prediction horizon) future outputs, assuming no future moves, and using the same model to compute the optimal M (control) horizon moves.Dynamic matrix control (DMC) (Cutler and Ramaker, 1980) is the most popular MPC algorithm used in chemical process industry today. Over the past decade, DMC has been implemented on a wide range of process applications. A major part of DMC's appeal in industry stems from the use of a linear finite step response model of the process and a simple quadratic performance objective function. The objective function is minimized over a prediction horizon to compute the optimal controller output moves as a least-squares problem.Tuning a controller is a direct way to reach its optimum performance. Tuning conventional controllers (P, PI, and PID) is related to obtain an optimum setting of controller parameters (controller gain K c , integral time T i , and derivative time T d ). Ziegler-Nichols, Lopez, Ciancone, etc. (Marlin, 2000) are some examples of single-loop tuning in P, PI, and PID controllers. Huang, et al. (2003) has proposed a

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Modeling and Control of Multivariable Distillation Column Using Model Predictive Control Using Unisim

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