Control of Inverse Response Process Using Model Predictive Controller

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

Fawwaz bin Fuat
Department of Chemical Engineering Universiti Teknologi PETRONAS Ipoh, Malaysia Abstract— Model predictive control is an important modelbased 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 controller (MPC) give a significant impact to the industrial practice, with over 4500 applications worldwide. MPC has choosen as one of the problem solver 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.

Dr. Lemma Dendena Tufa
Department of Chemical Engineering Universiti Teknologi PETRONAS Ipoh, Malaysia



A. Model Predictive Controller Structure Consider the typical thought process used by a human operator implementing a feedback control manually. The approach used by the operator has three important characteristics: It uses a model to come out with a predicted output that predicted from the manipulated value which then will compare with reference trajectory to produce future errors that will be optimize to become future inputs for the model and overall process. The important feedback information is act as corrective signal to the model after make comparison between the desired response and current the difference between the predicted model process responses. The control would be perfect if there is no difference between these two values. A continuous version of the approach can be automated with the general predictive control structure.

I. INTRODUCTION A general development is presented that gives great insight into the roles of both the control algorithm and the process in the behavior of feedback systems. It also provides a method for tailoring the feedback control algorithm to each specific application. Since the model of the process is an integral part of the control algorithm, the controller equation structure depends on the process model. Although the control algorithm is different, the feedback concept is unchanged, and the selection criteria for manipulated and controlled variables are the same. The algorithms could be used as replacements for the PID controller in nearly all applications. PID controller algorithm is considered the standard algorithm. Alternative algorithm is selected only when it provides better performance.

Figure 1. Predictive Controller Structure

The variable Emis equal to the disturbance effect, Gd(s)D(s), while (Gm(s)=Gp (s)) means the model is perfect. The structure highlights the disturbance for feedback correction. However, the model is essentially never exact. The feedback signal is affected by the disturbance and the model error, or mismatch.

The feedback signal also can be considered as a model correction for the process by correcting the set point so as to provide a better target value, Tp(s), to the predictive control...
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