Tuning Parameters of Dynamic Matrix Control

Abstract

Model predictive control (MPC) was developed to meet control challenges of Chemical Industries. With the passage of time, it has become the one of the most effective advanced control technique for a wide range of industries. The advantages of MPC are most evident when it is used as a multivariable controller integrated with an optimizer. Dynamic Matrix Control (DMC) was the first Model Predictive Control (MPC) algorithm developed by Shell Oil Company in 1970s. The advantages of these methods have already been proven and these methods have been found to work satisfactorily for long durations of time. DMC is available in all industrial process control systems and on a number of control simulation platforms. DMC is particularly advantageous for Multiple Input Multiple Output (MIMO) Systems.

This work deals with DMC for an unconstrained Single Input Single Output (SISO) system. Finite step Response (FSR) has been used for model prediction. Quadratic objective function, based upon the square of differences between predicted outputs and set-point, has been used. DMC is subsequently applied for the optimization of the objective function. It has shown that DMC has got inbuilt compensation for systems with dead time and systems with non-minimum phase. The effect of Tuning Parameters of Dynamic Matrix Control on the response of the system has been taken up. Various models have been simulated on MATLAB for this purpose. Effect of Noise on the system has been also studied. Modeling of a DC motor has been done at the end and an optimal set of Tuning Parameters has been found out. Further work is required to be done towards the application of this method to constrained systems and Multiple Input Multiple Output (MIMO) systems.