Application Research Of Predictive Control In Industrial Process

The theory of classical control and modern control are required to build up an accurate mathematical model. However, the actual industrial processes are complex processes of multivariable, strong-coupling, time-variant and constraint. We could not get its accurate mathematical model. In order to overcome the inconsonance between the theory and practical process, the control method with low required model and good effect combination control has been the research hot spot, and then predictive control came into being. Predictive control absorbs the optimization ideas of modern control theory, uses prediction models and rolling optimization and combine with feedback correction to replace the traditional optimal control. This strategy is very suitable for the control of complex industrial process.The three basic algorithms of predictive control are:model algorithmic control that based on nonparametric model of the object unit impulse response, dynamic matrix control that based on the object unit step response model (DMC) and generalized predictive control that uses the minimize parameter model (GPC). After the establishment of internal model, predictive control makes the system output the best result by rolling optimization and feedback correction continuously.It gave a fast algorithm for generalized predictive control by softening the control increment of generalized predictive control. This algorithm need not solve inverse matrix of the process of the parameter identification and reduce the amount of computation, so it is conducive to the realization of industrial field. Regarding the cold-rolled silicon steel strip coiling system of a heat treatment line in a Iron and Steel Group as the control object, we used the control algorithm to simulate the system on the condition of the interference that often appear in the strip winding process. The result shows that the improved GPC algorithm has good control performance in the event of random interference. We got the success of applying it to the actual coiling controller, and homogeneous degree of both sides of steel strip rolls have been improved obviously.The jigger production process has the characteristics of nonlinear, long time delay and large inertia, and has no effective mathematical model. Through analysis of the discharge process characteristics of the jigger, we introduce the grey models prediction into jigger fuzzy discharge control. According to analysis the dynamic change and operational trend of the jigger material thickness, we got the variation of the thickness and the forward predicted value of the thickness of the material in jigger bed. The predicting error and the predicting value of change rate of error can be got by comparing the predicted system output value with the reference value, and then import them into the fuzzy controller to implement. This control method implements ahead control and is particularly suitable for jigger discharge process. In this paper, the grey model prediction is derived, the fuzzy control rules are studied, and the grey prediction fuzzy control jig discharge controller was developed. These are all based on the study of control strategy. The bottom applied PLC control, it had two functions of numerical control air valve and the general fuzzy discharge controller. The host computer has functions of monitoring, controlling and controller with grey prediction fuzzy. Practical application shows that it has more evident improvement at control effect than using a single fuzzy controller.Dynamic Matrix Control (DMC) regards the object unit order step-response model which is measured easily in engineering as the internal model. It has the characteristics of simple algorithm, less computation and strong robustness;it is applicable to systems with pure time delay and non-minimum phase open-loop asymptotic stability. In view of a number of shortcomings for the DMC algorithm, improved algorithm of DMC and Dead Zone Prediction Error Correction DMC algorithm based on dead zone algorithm principle and error the prediction, these are based on the analysis of the basic algorithm principles of DMC. In this paper, the DMC algorithm is used to control the heating process in building and it verified the validity of the algorithm by the loop co-simulation tests.